├── README.md ├── configs ├── TSCTM.yaml └── TSCTM_aug.yaml ├── data └── GoogleNews-T │ ├── train_labels.txt │ ├── train_texts.txt │ ├── train_texts.txt_contextual0.3 │ ├── train_texts.txt_wordnet0.3 │ └── vocab.txt ├── models ├── TSC.py ├── TSCTM.py └── TopicDistQuant.py ├── run.py ├── runners └── Runner.py └── utils ├── Data.py ├── TU.py └── data_utils.py /README.md: -------------------------------------------------------------------------------- 1 | # Code for Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning (EMNLP 2022) 2 | 3 | ## **Check our latest topic modeling toolkit [TopMost](https://github.com/bobxwu/topmost) !** 4 | 5 | [EMNLP 2022](https://aclanthology.org/2022.emnlp-main.176) 6 | 7 | 8 | ## Usage 9 | 10 | ### 1. Prepare Environment 11 | 12 | python==3.7 13 | pytorch==1.7.1 14 | scipy==1.7.3 15 | scikit-learn==0.23.2 16 | pyyaml==6.0 17 | 18 | 19 | ### 2. Training 20 | 21 | Training without data augmentation: 22 | 23 | python run.py --data_dir data/${dataset} --model TSCTM --num_topic {number of topic} 24 | 25 | Training with data augmentation: 26 | 27 | python run.py --data_dir data/${dataset} --model TSCTM_aug --num_topic {number of topic} 28 | 29 | 30 | After training, outputs are in `./output/{dataset}/`, including topic words, topic distributions of short texts, and topic-word distribution matrix. 31 | 32 | 33 | ### 3. Evaluation 34 | 35 | **topic coherence**: [coherence score](https://github.com/dice-group/Palmetto) 36 | 37 | 38 | **topic diversity**: 39 | 40 | python utils/TU.py --data_path {path of topic word file} 41 | 42 | 43 | ## Citation 44 | 45 | If you want to use our code, please cite as 46 | 47 | @inproceedings{wu-etal-2022-mitigating, 48 | title = "Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning", 49 | author = "Wu, Xiaobao and 50 | Luu, Anh Tuan and 51 | Dong, Xinshuai", 52 | booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", 53 | month = dec, 54 | year = "2022", 55 | address = "Abu Dhabi, United Arab Emirates", 56 | publisher = "Association for Computational Linguistics", 57 | url = "https://aclanthology.org/2022.emnlp-main.176", 58 | pages = "2748--2760", 59 | } 60 | -------------------------------------------------------------------------------- /configs/TSCTM.yaml: -------------------------------------------------------------------------------- 1 | use_aug: False 2 | 3 | temperature: 0.5 4 | weight_contrast: 1.0 5 | 6 | batch_size: 128 7 | -------------------------------------------------------------------------------- /configs/TSCTM_aug.yaml: -------------------------------------------------------------------------------- 1 | use_aug: True 2 | 3 | temperature: 0.07 4 | weight_contrast: 3.0 5 | weight_same_quant: 0.001 6 | 7 | batch_size: 128 8 | -------------------------------------------------------------------------------- /data/GoogleNews-T/train_labels.txt: -------------------------------------------------------------------------------- 1 | 65 2 | 96 3 | 72 4 | 140 5 | 89 6 | 75 7 | 48 8 | 99 9 | 48 10 | 19 11 | 20 12 | 66 13 | 93 14 | 21 15 | 1 16 | 80 17 | 87 18 | 71 19 | 80 20 | 8 21 | 73 22 | 98 23 | 79 24 | 62 25 | 85 26 | 29 27 | 104 28 | 140 29 | 31 30 | 106 31 | 118 32 | 65 33 | 38 34 | 43 35 | 96 36 | 2 37 | 91 38 | 14 39 | 62 40 | 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| 8 1926 | 64 1927 | 11 1928 | 67 1929 | 16 1930 | 96 1931 | 71 1932 | 74 1933 | 90 1934 | 147 1935 | 1 1936 | 88 1937 | 19 1938 | 69 1939 | 119 1940 | 106 1941 | 133 1942 | 37 1943 | 96 1944 | 104 1945 | 95 1946 | 73 1947 | 90 1948 | 74 1949 | 92 1950 | 71 1951 | 68 1952 | 128 1953 | 118 1954 | 149 1955 | 91 1956 | 74 1957 | 30 1958 | 19 1959 | 77 1960 | 21 1961 | 8 1962 | 89 1963 | 21 1964 | 29 1965 | 50 1966 | 124 1967 | 10 1968 | 89 1969 | 2 1970 | 95 1971 | 77 1972 | 10 1973 | 65 1974 | 147 1975 | 128 1976 | 33 1977 | 148 1978 | 11 1979 | 82 1980 | 2 1981 | 53 1982 | 37 1983 | 29 1984 | 129 1985 | 73 1986 | 65 1987 | 33 1988 | 95 1989 | 143 1990 | 91 1991 | 89 1992 | 5 1993 | 1 1994 | 17 1995 | 91 1996 | 99 1997 | 21 1998 | 48 1999 | 42 2000 | 91 2001 | 99 2002 | 139 2003 | 52 2004 | 74 2005 | 70 2006 | 68 2007 | 87 2008 | 65 2009 | 133 2010 | 26 2011 | 146 2012 | 9 2013 | 89 2014 | 11 2015 | 12 2016 | 21 2017 | 42 2018 | 95 2019 | 90 2020 | 49 2021 | 127 2022 | 68 2023 | 93 2024 | 84 2025 | 17 2026 | 65 2027 | 44 2028 | 4 2029 | 26 2030 | 46 2031 | 18 2032 | 48 2033 | 130 2034 | 74 2035 | 83 2036 | 89 2037 | 141 2038 | 10 2039 | 139 2040 | 31 2041 | 129 2042 | 38 2043 | 43 2044 | 74 2045 | 26 2046 | 49 2047 | 66 2048 | 48 2049 | 100 2050 | 15 2051 | 152 2052 | 66 2053 | 42 2054 | 42 2055 | 139 2056 | 87 2057 | 33 2058 | 29 2059 | 53 2060 | 48 2061 | 140 2062 | 149 2063 | 141 2064 | 38 2065 | 2 2066 | 66 2067 | 129 2068 | 98 2069 | 77 2070 | 67 2071 | 48 2072 | 144 2073 | 140 2074 | 25 2075 | 95 2076 | 53 2077 | 1 2078 | 16 2079 | 80 2080 | 48 2081 | 85 2082 | 147 2083 | 17 2084 | 129 2085 | 93 2086 | 1 2087 | 96 2088 | 17 2089 | 139 2090 | 70 2091 | 68 2092 | 89 2093 | 89 2094 | 50 2095 | 77 2096 | 148 2097 | 33 2098 | 29 2099 | 24 2100 | 107 2101 | 77 2102 | 95 2103 | 82 2104 | 148 2105 | 44 2106 | 37 2107 | 73 2108 | 87 2109 | 88 2110 | 107 2111 | 27 2112 | 23 2113 | 13 2114 | 53 2115 | 98 2116 | 76 2117 | 68 2118 | 64 2119 | 89 2120 | 8 2121 | 95 2122 | 90 2123 | 88 2124 | 77 2125 | 44 2126 | 42 2127 | 104 2128 | 51 2129 | 102 2130 | 13 2131 | 87 2132 | 90 2133 | 42 2134 | 71 2135 | 29 2136 | 20 2137 | 89 2138 | 87 2139 | 143 2140 | 89 2141 | 121 2142 | 79 2143 | 80 2144 | 8 2145 | 29 2146 | 1 2147 | 77 2148 | 84 2149 | 21 2150 | 29 2151 | 1 2152 | 100 2153 | 15 2154 | 129 2155 | 21 2156 | 2 2157 | 120 2158 | 65 2159 | 79 2160 | 94 2161 | 94 2162 | 105 2163 | 46 2164 | 148 2165 | 66 2166 | 42 2167 | 8 2168 | 38 2169 | 74 2170 | 18 2171 | 79 2172 | 63 2173 | 48 2174 | 86 2175 | 98 2176 | 2 2177 | 139 2178 | 5 2179 | 112 2180 | 87 2181 | 43 2182 | 44 2183 | 7 2184 | 87 2185 | 79 2186 | 42 2187 | 86 2188 | 43 2189 | 2 2190 | 96 2191 | 96 2192 | 8 2193 | 87 2194 | 50 2195 | 125 2196 | 10 2197 | 42 2198 | 77 2199 | 133 2200 | 79 2201 | 93 2202 | 18 2203 | 77 2204 | 42 2205 | 64 2206 | 16 2207 | 41 2208 | 16 2209 | 21 2210 | 12 2211 | 42 2212 | 142 2213 | 1 2214 | 118 2215 | 42 2216 | 93 2217 | 149 2218 | 79 2219 | 53 2220 | 73 2221 | 127 2222 | 65 2223 | 105 2224 | 68 2225 | 10 2226 | 135 2227 | 30 2228 | 142 2229 | 73 2230 | 73 2231 | 8 2232 | 124 2233 | 42 2234 | 63 2235 | 48 2236 | 146 2237 | 8 2238 | 69 2239 | 68 2240 | 1 2241 | 29 2242 | 10 2243 | 102 2244 | 21 2245 | 17 2246 | 121 2247 | 141 2248 | 90 2249 | 4 2250 | 62 2251 | 64 2252 | 14 2253 | 43 2254 | 42 2255 | 139 2256 | 62 2257 | 42 2258 | 139 2259 | 149 2260 | 24 2261 | 42 2262 | 17 2263 | 87 2264 | 82 2265 | 38 2266 | 42 2267 | 70 2268 | 87 2269 | 18 2270 | 140 2271 | 4 2272 | 73 2273 | 49 2274 | 30 2275 | 36 2276 | 12 2277 | 65 2278 | 79 2279 | 70 2280 | 93 2281 | 79 2282 | 9 2283 | 144 2284 | 4 2285 | 95 2286 | 129 2287 | 21 2288 | 75 2289 | 48 2290 | 74 2291 | 5 2292 | 48 2293 | 93 2294 | 28 2295 | 132 2296 | 16 2297 | 8 2298 | 65 2299 | 118 2300 | 117 2301 | 66 2302 | 66 2303 | 89 2304 | 67 2305 | 2 2306 | 64 2307 | 71 2308 | 106 2309 | 70 2310 | 91 2311 | 8 2312 | 74 2313 | 1 2314 | 29 2315 | 63 2316 | 95 2317 | 104 2318 | 65 2319 | 68 2320 | 53 2321 | 114 2322 | 139 2323 | 73 2324 | 74 2325 | 77 2326 | 33 2327 | 140 2328 | 41 2329 | 127 2330 | 13 2331 | 52 2332 | 8 2333 | 15 2334 | 74 2335 | 66 2336 | 61 2337 | 122 2338 | 89 2339 | 84 2340 | 93 2341 | 42 2342 | 89 2343 | 53 2344 | 16 2345 | 79 2346 | 25 2347 | 86 2348 | 85 2349 | 93 2350 | 96 2351 | 1 2352 | 77 2353 | 8 2354 | 21 2355 | 139 2356 | 79 2357 | 68 2358 | 77 2359 | 87 2360 | 80 2361 | 42 2362 | 64 2363 | 5 2364 | 124 2365 | 115 2366 | 71 2367 | 9 2368 | 93 2369 | 84 2370 | 2 2371 | 102 2372 | 77 2373 | 65 2374 | 77 2375 | 48 2376 | 86 2377 | 16 2378 | 42 2379 | 65 2380 | 98 2381 | 5 2382 | 128 2383 | 29 2384 | 93 2385 | 42 2386 | 27 2387 | 93 2388 | 20 2389 | 1 2390 | 70 2391 | 93 2392 | 27 2393 | 5 2394 | 151 2395 | 120 2396 | 11 2397 | 5 2398 | 77 2399 | 77 2400 | 48 2401 | 70 2402 | 48 2403 | 30 2404 | 129 2405 | 139 2406 | 77 2407 | 18 2408 | 95 2409 | 48 2410 | 21 2411 | 39 2412 | 75 2413 | 77 2414 | 16 2415 | 124 2416 | 74 2417 | 74 2418 | 62 2419 | 42 2420 | 96 2421 | 71 2422 | 36 2423 | 1 2424 | 48 2425 | 68 2426 | 60 2427 | 89 2428 | 8 2429 | 95 2430 | 86 2431 | 6 2432 | 110 2433 | 73 2434 | 73 2435 | 96 2436 | 90 2437 | 70 2438 | 15 2439 | 46 2440 | 98 2441 | 55 2442 | 21 2443 | 68 2444 | 67 2445 | 102 2446 | 1 2447 | 4 2448 | 127 2449 | 64 2450 | 69 2451 | 2 2452 | 61 2453 | 10 2454 | 18 2455 | 68 2456 | 67 2457 | 96 2458 | 140 2459 | 48 2460 | 4 2461 | 68 2462 | 90 2463 | 127 2464 | 89 2465 | 142 2466 | 73 2467 | 21 2468 | 27 2469 | 152 2470 | 129 2471 | 77 2472 | 107 2473 | 114 2474 | 17 2475 | 68 2476 | 31 2477 | 135 2478 | 124 2479 | 25 2480 | 42 2481 | 42 2482 | 85 2483 | 2 2484 | 95 2485 | 89 2486 | 67 2487 | 29 2488 | 29 2489 | 21 2490 | 46 2491 | 113 2492 | 80 2493 | 33 2494 | 131 2495 | 79 2496 | 149 2497 | 16 2498 | 74 2499 | 124 2500 | 118 2501 | 139 2502 | 26 2503 | 21 2504 | 89 2505 | 4 2506 | 129 2507 | 98 2508 | 93 2509 | 29 2510 | 139 2511 | 33 2512 | 142 2513 | 82 2514 | 2 2515 | 73 2516 | 1 2517 | 132 2518 | 46 2519 | 12 2520 | 129 2521 | 21 2522 | 68 2523 | 77 2524 | 38 2525 | 68 2526 | 134 2527 | 1 2528 | 90 2529 | 87 2530 | 85 2531 | 90 2532 | 42 2533 | 2 2534 | 77 2535 | 43 2536 | 70 2537 | 115 2538 | 99 2539 | 136 2540 | 148 2541 | 90 2542 | 15 2543 | 17 2544 | 29 2545 | 8 2546 | 43 2547 | 64 2548 | 97 2549 | 29 2550 | 33 2551 | 128 2552 | 73 2553 | 85 2554 | 98 2555 | 93 2556 | 29 2557 | 131 2558 | 94 2559 | 8 2560 | 19 2561 | 62 2562 | 149 2563 | 94 2564 | 97 2565 | 42 2566 | 93 2567 | 76 2568 | 90 2569 | 48 2570 | 142 2571 | 14 2572 | 121 2573 | 39 2574 | 11 2575 | 21 2576 | 93 2577 | 77 2578 | 140 2579 | 68 2580 | 65 2581 | 17 2582 | 110 2583 | 68 2584 | 96 2585 | 67 2586 | 16 2587 | 96 2588 | 93 2589 | 48 2590 | 122 2591 | 67 2592 | 43 2593 | 68 2594 | 89 2595 | 21 2596 | 131 2597 | 73 2598 | 103 2599 | 67 2600 | 83 2601 | 42 2602 | 104 2603 | 87 2604 | 48 2605 | 4 2606 | 68 2607 | 71 2608 | 1 2609 | 77 2610 | 9 2611 | 89 2612 | 44 2613 | 70 2614 | 105 2615 | 42 2616 | 8 2617 | 53 2618 | 17 2619 | 103 2620 | 126 2621 | 127 2622 | 96 2623 | 89 2624 | 4 2625 | 17 2626 | 131 2627 | 88 2628 | 140 2629 | 87 2630 | 85 2631 | 75 2632 | 21 2633 | 67 2634 | 95 2635 | 128 2636 | 10 2637 | 21 2638 | 77 2639 | 5 2640 | 17 2641 | 14 2642 | 96 2643 | 4 2644 | 13 2645 | 1 2646 | 48 2647 | 71 2648 | 21 2649 | 96 2650 | 17 2651 | 91 2652 | 64 2653 | 46 2654 | 121 2655 | 91 2656 | 62 2657 | 44 2658 | 124 2659 | 4 2660 | 149 2661 | 79 2662 | 89 2663 | 121 2664 | 77 2665 | 80 2666 | 145 2667 | 38 2668 | 58 2669 | 88 2670 | 140 2671 | 32 2672 | 25 2673 | 93 2674 | 13 2675 | 74 2676 | 77 2677 | 10 2678 | 64 2679 | 96 2680 | 89 2681 | 73 2682 | 142 2683 | 15 2684 | 11 2685 | 25 2686 | 57 2687 | 148 2688 | 140 2689 | 68 2690 | 143 2691 | 89 2692 | 125 2693 | 113 2694 | 89 2695 | 62 2696 | 85 2697 | 46 2698 | 70 2699 | 91 2700 | 75 2701 | 41 2702 | 71 2703 | 13 2704 | 79 2705 | 139 2706 | 95 2707 | 96 2708 | 72 2709 | 68 2710 | 45 2711 | 66 2712 | 32 2713 | 15 2714 | 42 2715 | 71 2716 | 17 2717 | 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2916 | 62 2917 | 122 2918 | 74 2919 | 1 2920 | 37 2921 | 21 2922 | 21 2923 | 77 2924 | 139 2925 | 17 2926 | 1 2927 | 77 2928 | 48 2929 | 128 2930 | 69 2931 | 58 2932 | 1 2933 | 129 2934 | 77 2935 | 102 2936 | 107 2937 | 48 2938 | 97 2939 | 1 2940 | 73 2941 | 96 2942 | 79 2943 | 64 2944 | 58 2945 | 96 2946 | 65 2947 | 91 2948 | 118 2949 | 90 2950 | 53 2951 | 53 2952 | 68 2953 | 89 2954 | 16 2955 | 4 2956 | 51 2957 | 8 2958 | 68 2959 | 74 2960 | 51 2961 | 29 2962 | 143 2963 | 146 2964 | 63 2965 | 95 2966 | 42 2967 | 89 2968 | 146 2969 | 2 2970 | 8 2971 | 4 2972 | 89 2973 | 128 2974 | 124 2975 | 1 2976 | 127 2977 | 91 2978 | 21 2979 | 77 2980 | 108 2981 | 77 2982 | 97 2983 | 12 2984 | 34 2985 | 139 2986 | 74 2987 | 16 2988 | 93 2989 | 1 2990 | 87 2991 | 67 2992 | 21 2993 | 91 2994 | 91 2995 | 106 2996 | 110 2997 | 33 2998 | 121 2999 | 101 3000 | 89 3001 | 42 3002 | 3 3003 | 73 3004 | 102 3005 | 29 3006 | 21 3007 | 72 3008 | 38 3009 | 15 3010 | 139 3011 | 90 3012 | 37 3013 | 8 3014 | 135 3015 | 63 3016 | 93 3017 | 69 3018 | 77 3019 | 36 3020 | 6 3021 | 104 3022 | 114 3023 | 1 3024 | 140 3025 | 48 3026 | 139 3027 | 44 3028 | 33 3029 | 59 3030 | 104 3031 | 79 3032 | 75 3033 | 96 3034 | 75 3035 | 15 3036 | 48 3037 | 62 3038 | 8 3039 | 30 3040 | 17 3041 | 130 3042 | 64 3043 | 144 3044 | 30 3045 | 50 3046 | 139 3047 | 127 3048 | 21 3049 | 8 3050 | 27 3051 | 14 3052 | 152 3053 | 87 3054 | 8 3055 | 57 3056 | 68 3057 | 82 3058 | 21 3059 | 95 3060 | 17 3061 | 61 3062 | 149 3063 | 70 3064 | 18 3065 | 139 3066 | 54 3067 | 89 3068 | 48 3069 | 65 3070 | 84 3071 | 72 3072 | 89 3073 | 151 3074 | 89 3075 | 85 3076 | 14 3077 | 41 3078 | 94 3079 | 71 3080 | 2 3081 | 139 3082 | 95 3083 | 1 3084 | 144 3085 | 48 3086 | 25 3087 | 90 3088 | 76 3089 | 77 3090 | 48 3091 | 42 3092 | 89 3093 | 74 3094 | 6 3095 | 15 3096 | 129 3097 | 28 3098 | 8 3099 | 149 3100 | 17 3101 | 42 3102 | 42 3103 | 27 3104 | 1 3105 | 146 3106 | 66 3107 | 11 3108 | 8 3109 | 79 3110 | 77 3111 | 102 3112 | 67 3113 | 93 3114 | 17 3115 | 89 3116 | 79 3117 | 70 3118 | 42 3119 | 93 3120 | 93 3121 | 121 3122 | 62 3123 | 10 3124 | 68 3125 | 95 3126 | 15 3127 | 16 3128 | 76 3129 | 65 3130 | 33 3131 | 136 3132 | 77 3133 | 65 3134 | 53 3135 | 52 3136 | 77 3137 | 65 3138 | 52 3139 | 48 3140 | 68 3141 | 53 3142 | 29 3143 | 143 3144 | 80 3145 | 86 3146 | 68 3147 | 66 3148 | 61 3149 | 118 3150 | 93 3151 | 21 3152 | 63 3153 | 95 3154 | 103 3155 | 32 3156 | 54 3157 | 30 3158 | 16 3159 | 87 3160 | 100 3161 | 101 3162 | 62 3163 | 105 3164 | 8 3165 | 42 3166 | 61 3167 | 16 3168 | 93 3169 | 42 3170 | 42 3171 | 119 3172 | 13 3173 | 90 3174 | 6 3175 | 48 3176 | 112 3177 | 68 3178 | 58 3179 | 21 3180 | 89 3181 | 49 3182 | 116 3183 | 25 3184 | 48 3185 | 27 3186 | 68 3187 | 5 3188 | 27 3189 | 87 3190 | 71 3191 | 74 3192 | 120 3193 | 87 3194 | 93 3195 | 1 3196 | 15 3197 | 30 3198 | 91 3199 | 25 3200 | 66 3201 | 89 3202 | 87 3203 | 42 3204 | 91 3205 | 2 3206 | 17 3207 | 61 3208 | 131 3209 | 43 3210 | 1 3211 | 73 3212 | 96 3213 | 80 3214 | 21 3215 | 42 3216 | 21 3217 | 77 3218 | 96 3219 | 65 3220 | 70 3221 | 48 3222 | 89 3223 | 1 3224 | 25 3225 | 68 3226 | 29 3227 | 30 3228 | 62 3229 | 17 3230 | 15 3231 | 77 3232 | 62 3233 | 93 3234 | 82 3235 | 15 3236 | 11 3237 | 8 3238 | 129 3239 | 93 3240 | 121 3241 | 1 3242 | 90 3243 | 1 3244 | 1 3245 | 74 3246 | 139 3247 | 145 3248 | 127 3249 | 17 3250 | 28 3251 | 42 3252 | 48 3253 | 73 3254 | 8 3255 | 70 3256 | 42 3257 | 61 3258 | 69 3259 | 1 3260 | 101 3261 | 146 3262 | 90 3263 | 140 3264 | 122 3265 | 2 3266 | 2 3267 | 96 3268 | 6 3269 | 100 3270 | 91 3271 | 46 3272 | 140 3273 | 119 3274 | 74 3275 | 77 3276 | 86 3277 | 9 3278 | 96 3279 | 121 3280 | 89 3281 | 25 3282 | 12 3283 | 99 3284 | 131 3285 | 21 3286 | 17 3287 | 8 3288 | 69 3289 | 42 3290 | 91 3291 | 139 3292 | 119 3293 | 7 3294 | 13 3295 | 74 3296 | 17 3297 | 21 3298 | 46 3299 | 43 3300 | 129 3301 | 21 3302 | 77 3303 | 68 3304 | 7 3305 | 94 3306 | 139 3307 | 4 3308 | 52 3309 | 53 3310 | 94 3311 | 48 3312 | 139 3313 | 62 3314 | 53 3315 | 6 3316 | 8 3317 | 44 3318 | 13 3319 | 41 3320 | 63 3321 | 21 3322 | 21 3323 | 79 3324 | 6 3325 | 62 3326 | 69 3327 | 48 3328 | 8 3329 | 140 3330 | 27 3331 | 15 3332 | 97 3333 | 69 3334 | 21 3335 | 150 3336 | 151 3337 | 73 3338 | 85 3339 | 65 3340 | 152 3341 | 143 3342 | 80 3343 | 70 3344 | 62 3345 | 21 3346 | 18 3347 | 86 3348 | 120 3349 | 14 3350 | 68 3351 | 123 3352 | 73 3353 | 62 3354 | 11 3355 | 119 3356 | 71 3357 | 146 3358 | 59 3359 | 52 3360 | 36 3361 | 77 3362 | 87 3363 | 53 3364 | 21 3365 | 77 3366 | 148 3367 | 48 3368 | 8 3369 | 21 3370 | 127 3371 | 91 3372 | 89 3373 | 144 3374 | 4 3375 | 29 3376 | 89 3377 | 86 3378 | 79 3379 | 145 3380 | 86 3381 | 88 3382 | 2 3383 | 42 3384 | 139 3385 | 68 3386 | 42 3387 | 18 3388 | 91 3389 | 65 3390 | 129 3391 | 18 3392 | 139 3393 | 87 3394 | 121 3395 | 70 3396 | 68 3397 | 141 3398 | 2 3399 | 127 3400 | 2 3401 | 67 3402 | 87 3403 | 17 3404 | 43 3405 | 17 3406 | 94 3407 | 89 3408 | 91 3409 | 83 3410 | 73 3411 | 73 3412 | 48 3413 | 89 3414 | 73 3415 | 96 3416 | 89 3417 | 95 3418 | 73 3419 | 50 3420 | 21 3421 | 29 3422 | 91 3423 | 89 3424 | 2 3425 | 6 3426 | 65 3427 | 78 3428 | 8 3429 | 41 3430 | 46 3431 | 61 3432 | 42 3433 | 77 3434 | 124 3435 | 1 3436 | 94 3437 | 42 3438 | 68 3439 | 23 3440 | 65 3441 | 74 3442 | 21 3443 | 37 3444 | 43 3445 | 102 3446 | 34 3447 | 68 3448 | 70 3449 | 129 3450 | 15 3451 | 67 3452 | 124 3453 | 67 3454 | 73 3455 | 92 3456 | 77 3457 | 93 3458 | 79 3459 | 130 3460 | 116 3461 | 77 3462 | 53 3463 | 82 3464 | 93 3465 | 30 3466 | 65 3467 | 96 3468 | 68 3469 | 89 3470 | 69 3471 | 95 3472 | 143 3473 | 140 3474 | 100 3475 | 139 3476 | 70 3477 | 20 3478 | 10 3479 | 12 3480 | 150 3481 | 151 3482 | 20 3483 | 42 3484 | 11 3485 | 1 3486 | 88 3487 | 118 3488 | 27 3489 | 17 3490 | 120 3491 | 64 3492 | 96 3493 | 8 3494 | 96 3495 | 48 3496 | 93 3497 | 88 3498 | 6 3499 | 31 3500 | 139 3501 | 104 3502 | 52 3503 | 42 3504 | 92 3505 | 2 3506 | 2 3507 | 88 3508 | 7 3509 | 65 3510 | 89 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42 10681 | 86 10682 | 21 10683 | 141 10684 | 77 10685 | 90 10686 | 21 10687 | 86 10688 | 129 10689 | 2 10690 | 89 10691 | 42 10692 | 131 10693 | 121 10694 | 149 10695 | 42 10696 | 53 10697 | 70 10698 | 98 10699 | 56 10700 | 88 10701 | 97 10702 | 53 10703 | 107 10704 | 136 10705 | 11 10706 | 68 10707 | 133 10708 | 90 10709 | 114 10710 | 89 10711 | 23 10712 | 146 10713 | 89 10714 | 104 10715 | 8 10716 | 57 10717 | 74 10718 | 75 10719 | 16 10720 | 96 10721 | 48 10722 | 143 10723 | 135 10724 | 1 10725 | 6 10726 | 1 10727 | 139 10728 | 41 10729 | 79 10730 | 65 10731 | 93 10732 | 63 10733 | 91 10734 | 19 10735 | 8 10736 | 71 10737 | 82 10738 | 70 10739 | 127 10740 | 3 10741 | 77 10742 | 88 10743 | 86 10744 | 62 10745 | 67 10746 | 42 10747 | 16 10748 | 5 10749 | 87 10750 | 21 10751 | 42 10752 | 133 10753 | 68 10754 | 21 10755 | 101 10756 | 64 10757 | 70 10758 | 20 10759 | 124 10760 | 10 10761 | 94 10762 | 95 10763 | 28 10764 | 33 10765 | 70 10766 | 65 10767 | 60 10768 | 129 10769 | 4 10770 | 115 10771 | 124 10772 | 8 10773 | 146 10774 | 62 10775 | 77 10776 | 75 10777 | 135 10778 | 6 10779 | 4 10780 | 44 10781 | 42 10782 | 140 10783 | 93 10784 | 42 10785 | 3 10786 | 79 10787 | 21 10788 | 6 10789 | 96 10790 | 61 10791 | 93 10792 | 118 10793 | 62 10794 | 73 10795 | 83 10796 | 95 10797 | 68 10798 | 148 10799 | 65 10800 | 42 10801 | 88 10802 | 98 10803 | 129 10804 | 143 10805 | 80 10806 | 43 10807 | 28 10808 | 80 10809 | 42 10810 | 86 10811 | 108 10812 | 8 10813 | 89 10814 | 8 10815 | 88 10816 | 64 10817 | 87 10818 | 95 10819 | 6 10820 | 18 10821 | 145 10822 | 12 10823 | 8 10824 | 90 10825 | 119 10826 | 6 10827 | 111 10828 | 27 10829 | 30 10830 | 93 10831 | 17 10832 | 96 10833 | 70 10834 | 96 10835 | 86 10836 | 27 10837 | 139 10838 | 6 10839 | 27 10840 | 34 10841 | 43 10842 | 48 10843 | 2 10844 | 64 10845 | 4 10846 | 88 10847 | 65 10848 | 53 10849 | 77 10850 | 25 10851 | 74 10852 | 8 10853 | 8 10854 | 15 10855 | 151 10856 | 98 10857 | 77 10858 | 97 10859 | 33 10860 | 100 10861 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| play 1602 | player 1603 | playing 1604 | playoff 1605 | playstation 1606 | plea 1607 | plead 1608 | pleads 1609 | pledge 1610 | plot 1611 | pm 1612 | poaching 1613 | point 1614 | poised 1615 | police 1616 | policy 1617 | political 1618 | politics 1619 | poll 1620 | poor 1621 | pop 1622 | pope 1623 | popular 1624 | porn 1625 | pose 1626 | position 1627 | positive 1628 | post 1629 | potential 1630 | pound 1631 | poverty 1632 | power 1633 | practice 1634 | praise 1635 | prayer 1636 | pre 1637 | predator 1638 | prediction 1639 | premier 1640 | prepare 1641 | prepares 1642 | presence 1643 | president 1644 | press 1645 | pressure 1646 | pretty 1647 | prevent 1648 | preview 1649 | prey 1650 | price 1651 | prime 1652 | primesense 1653 | prince 1654 | princess 1655 | princeton 1656 | printed 1657 | printing 1658 | priority 1659 | prison 1660 | private 1661 | pro 1662 | probable 1663 | probe 1664 | problem 1665 | producer 1666 | production 1667 | prof 1668 | profanity 1669 | profile 1670 | profit 1671 | program 1672 | programme 1673 | progress 1674 | progression 1675 | project 1676 | projection 1677 | promise 1678 | prompt 1679 | proposal 1680 | proposed 1681 | proposes 1682 | prostate 1683 | protecting 1684 | protest 1685 | protester 1686 | protosevich 1687 | provide 1688 | psalm 1689 | public 1690 | pull 1691 | punch 1692 | punt 1693 | purchase 1694 | push 1695 | put 1696 | putin 1697 | putting 1698 | qa 1699 | qb 1700 | quad 1701 | qualify 1702 | quarter 1703 | quarterback 1704 | quarterly 1705 | queen 1706 | question 1707 | quick 1708 | quote 1709 | rabbi 1710 | race 1711 | racing 1712 | radical 1713 | radicalisers 1714 | radicalizers 1715 | radio 1716 | raft 1717 | raheel 1718 | raid 1719 | raider 1720 | rain 1721 | raise 1722 | raised 1723 | rajesh 1724 | rally 1725 | ram 1726 | rant 1727 | rape 1728 | raping 1729 | raptor 1730 | rare 1731 | rate 1732 | rating 1733 | raven 1734 | ray 1735 | reach 1736 | react 1737 | reaction 1738 | reacts 1739 | read 1740 | ready 1741 | real 1742 | reality 1743 | reason 1744 | rebel 1745 | recap 1746 | receives 1747 | recipe 1748 | record 1749 | recovery 1750 | recreate 1751 | recreated 1752 | red 1753 | redbox 1754 | reduce 1755 | referendum 1756 | reform 1757 | refugee 1758 | refuse 1759 | regime 1760 | region 1761 | regional 1762 | regret 1763 | regular 1764 | regulation 1765 | rein 1766 | reject 1767 | relationship 1768 | release 1769 | released 1770 | relief 1771 | relieved 1772 | religious 1773 | remain 1774 | remains 1775 | remake 1776 | remark 1777 | remnant 1778 | remote 1779 | replace 1780 | replaces 1781 | report 1782 | reported 1783 | reportedly 1784 | reporter 1785 | republic 1786 | request 1787 | rescue 1788 | researcher 1789 | reserve 1790 | resident 1791 | resigns 1792 | resolve 1793 | resolved 1794 | responds 1795 | response 1796 | restriction 1797 | resume 1798 | retail 1799 | retailer 1800 | retracts 1801 | return 1802 | returning 1803 | reveal 1804 | revealed 1805 | reveals 1806 | revenge 1807 | revenue 1808 | reverse 1809 | review 1810 | reviewed 1811 | rice 1812 | rich 1813 | rift 1814 | riga 1815 | right 1816 | rihanna 1817 | riley 1818 | rip 1819 | rise 1820 | rising 1821 | risk 1822 | rival 1823 | rivalry 1824 | river 1825 | road 1826 | robert 1827 | robinson 1828 | robot 1829 | rock 1830 | rocker 1831 | rocket 1832 | rockstar 1833 | rodgers 1834 | rogan 1835 | rogen 1836 | role 1837 | roll 1838 | rolling 1839 | romance 1840 | rome 1841 | romeo 1842 | romo 1843 | roof 1844 | room 1845 | rope 1846 | rose 1847 | rough 1848 | round 1849 | roundup 1850 | rover 1851 | row 1852 | royal 1853 | royalty 1854 | ruin 1855 | rule 1856 | ruled 1857 | rumor 1858 | rumored 1859 | running 1860 | rush 1861 | rushing 1862 | russia 1863 | russian 1864 | ryan 1865 | saatchi 1866 | sachs 1867 | safe 1868 | salah 1869 | sale 1870 | salmond 1871 | salt 1872 | samsung 1873 | san 1874 | sanction 1875 | sandidge 1876 | sandy 1877 | satellite 1878 | sauce 1879 | save 1880 | saving 1881 | 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| shipping 1952 | shipwreck 1953 | shirtless 1954 | shock 1955 | shocking 1956 | shooter 1957 | shooting 1958 | shop 1959 | shopper 1960 | shopping 1961 | short 1962 | shot 1963 | shumpert 1964 | shut 1965 | shutdown 1966 | side 1967 | sight 1968 | sign 1969 | signature 1970 | signed 1971 | signing 1972 | silvio 1973 | sim 1974 | sing 1975 | singer 1976 | singing 1977 | single 1978 | sings 1979 | sinn 1980 | sister 1981 | site 1982 | sitton 1983 | situation 1984 | ski 1985 | skid 1986 | skin 1987 | sky 1988 | skype 1989 | slam 1990 | slave 1991 | slaying 1992 | sleet 1993 | slim 1994 | slimane 1995 | slip 1996 | slow 1997 | slower 1998 | slowing 1999 | slur 2000 | small 2001 | smart 2002 | smartphone 2003 | smartphones 2004 | smartwig 2005 | smelly 2006 | smile 2007 | smith 2008 | smoking 2009 | snap 2010 | snarl 2011 | snow 2012 | snowden 2013 | snp 2014 | soar 2015 | soccer 2016 | sochi 2017 | social 2018 | sodium 2019 | software 2020 | soho 2021 | solar 2022 | sold 2023 | soldier 2024 | solid 2025 | solidarity 2026 | soluble 2027 | solution 2028 | son 2029 | song 2030 | sony 2031 | sought 2032 | sound 2033 | source 2034 | south 2035 | sp 2036 | space 2037 | spacex 2038 | spam 2039 | spark 2040 | spat 2041 | spd 2042 | speak 2043 | speaks 2044 | spec 2045 | special 2046 | specie 2047 | spectacular 2048 | speculation 2049 | speech 2050 | speed 2051 | spending 2052 | spice 2053 | spied 2054 | spike 2055 | spill 2056 | spin 2057 | spirit 2058 | split 2059 | spoof 2060 | sport 2061 | spot 2062 | spread 2063 | spree 2064 | spy 2065 | spying 2066 | square 2067 | sriracha 2068 | st 2069 | stabbed 2070 | stabbing 2071 | stability 2072 | stadium 2073 | staff 2074 | stage 2075 | stake 2076 | stallone 2077 | stand 2078 | standard 2079 | star 2080 | starring 2081 | start 2082 | starter 2083 | starting 2084 | startup 2085 | state 2086 | statement 2087 | statham 2088 | station 2089 | status 2090 | stay 2091 | stem 2092 | step 2093 | sterling 2094 | steubenville 2095 | stir 2096 | stock 2097 | store 2098 | storm 2099 | story 2100 | stoudemire 2101 | straight 2102 | strategy 2103 | streak 2104 | stream 2105 | streaming 2106 | street 2107 | strength 2108 | stress 2109 | stretch 2110 | strike 2111 | stroke 2112 | strong 2113 | struggle 2114 | struggling 2115 | stuckey 2116 | student 2117 | studio 2118 | study 2119 | stun 2120 | stuns 2121 | style 2122 | substance 2123 | success 2124 | sue 2125 | suffering 2126 | suffers 2127 | sugar 2128 | suggests 2129 | suicide 2130 | suit 2131 | suitor 2132 | summer 2133 | sun 2134 | sunday 2135 | super 2136 | superintendent 2137 | supermarket 2138 | supernova 2139 | supplement 2140 | supply 2141 | support 2142 | surface 2143 | surgery 2144 | surgical 2145 | surprise 2146 | surveillance 2147 | survey 2148 | survival 2149 | survive 2150 | survives 2151 | survivor 2152 | susan 2153 | suspect 2154 | suspected 2155 | suspended 2156 | suspension 2157 | suthep 2158 | swearing 2159 | swift 2160 | swine 2161 | swiss 2162 | sylvester 2163 | symptom 2164 | syria 2165 | syrian 2166 | system 2167 | table 2168 | tablet 2169 | tackle 2170 | tag 2171 | take 2172 | tale 2173 | talk 2174 | talked 2175 | talking 2176 | talwar 2177 | talwars 2178 | tampa 2179 | taper 2180 | target 2181 | targeting 2182 | tate 2183 | tax 2184 | taylor 2185 | tea 2186 | teach 2187 | team 2188 | teammate 2189 | teamreport 2190 | teardown 2191 | tease 2192 | tech 2193 | technical 2194 | teen 2195 | teenage 2196 | tell 2197 | ten 2198 | tension 2199 | term 2200 | terrorist 2201 | test 2202 | testing 2203 | texas 2204 | thai 2205 | thailand 2206 | thanksgiving 2207 | thanksgivukkah 2208 | theft 2209 | theory 2210 | thing 2211 | think 2212 | thinnest 2213 | third 2214 | thomas 2215 | thought 2216 | thousand 2217 | threat 2218 | threaten 2219 | threatened 2220 | threatens 2221 | three 2222 | thriller 2223 | throat 2224 | throw 2225 | thurmond 2226 | thursday 2227 | ticket 2228 | tie 2229 | tiffany 2230 | time 2231 | timeline 2232 | tiny 2233 | tip 2234 | title 2235 | tivo 2236 | today 2237 | toe 2238 | told 2239 | toll 2240 | tolzien 2241 | tom 2242 | tomorrow 2243 | tonight 2244 | tony 2245 | top 2246 | topless 2247 | toronto 2248 | torres 2249 | total 2250 | touchdown 2251 | tough 2252 | tour 2253 | tourism 2254 | town 2255 | track 2256 | trade 2257 | tradition 2258 | traditional 2259 | tragedy 2260 | train 2261 | transition 2262 | trap 2263 | travel 2264 | traveler 2265 | treasury 2266 | treatment 2267 | trend 2268 | trial 2269 | trickle 2270 | trip 2271 | troop 2272 | trophy 2273 | trouble 2274 | truck 2275 | true 2276 | trust 2277 | tucson 2278 | tuesday 2279 | turkey 2280 | turn 2281 | turnaround 2282 | turned 2283 | turning 2284 | turnover 2285 | turtle 2286 | tv 2287 | tweet 2288 | twin 2289 | twist 2290 | twitter 2291 | tycho 2292 | typhoon 2293 | tyranny 2294 | tyrese 2295 | uefa 2296 | uk 2297 | uma 2298 | uncertain 2299 | underwater 2300 | unemployment 2301 | unexpected 2302 | unexpectedly 2303 | union 2304 | unit 2305 | university 2306 | unknown 2307 | unlocked 2308 | unreported 2309 | unrest 2310 | unsure 2311 | unusual 2312 | unveiled 2313 | unveils 2314 | upcoming 2315 | update 2316 | updated 2317 | upload 2318 | urban 2319 | urge 2320 | urged 2321 | user 2322 | vaccination 2323 | vaccine 2324 | vatican 2325 | veldheer 2326 | vendetta 2327 | verdict 2328 | verizon 2329 | version 2330 | versus 2331 | victim 2332 | victory 2333 | video 2334 | view 2335 | viewing 2336 | viking 2337 | vinny 2338 | violation 2339 | violence 2340 | viral 2341 | vision 2342 | visit 2343 | vitamin 2344 | voice 2345 | vonn 2346 | vote 2347 | voter 2348 | vow 2349 | wage 2350 | wait 2351 | waiting 2352 | wake 2353 | wal 2354 | walk 2355 | wall 2356 | walmart 2357 | walter 2358 | wanted 2359 | war 2360 | warm 2361 | warned 2362 | warning 2363 | warns 2364 | warrant 2365 | warren 2366 | washington 2367 | watch 2368 | watching 2369 | water 2370 | watkins 2371 | wave 2372 | way 2373 | weapon 2374 | wear 2375 | wearhouse 2376 | weather 2377 | weatherford 2378 | web 2379 | wed 2380 | wedding 2381 | wednesday 2382 | week 2383 | weekend 2384 | weekly 2385 | weigh 2386 | weight 2387 | welcome 2388 | welfare 2389 | welker 2390 | well 2391 | wes 2392 | west 2393 | western 2394 | whitaker 2395 | white 2396 | whitman 2397 | whitney 2398 | wife 2399 | wig 2400 | wii 2401 | wild 2402 | wildcat 2403 | willard 2404 | william 2405 | williams 2406 | win 2407 | wind 2408 | window 2409 | winfield 2410 | winged 2411 | winner 2412 | winning 2413 | winston 2414 | winter 2415 | wintry 2416 | witness 2417 | wizard 2418 | wmu 2419 | woe 2420 | woman 2421 | wood 2422 | woodson 2423 | word 2424 | work 2425 | worker 2426 | working 2427 | worldwide 2428 | worried 2429 | worse 2430 | worst 2431 | worth 2432 | wounded 2433 | wr 2434 | written 2435 | wrong 2436 | xbox 2437 | yank 2438 | yankee 2439 | yard 2440 | year 2441 | yeezus 2442 | yellen 2443 | yen 2444 | yield 2445 | york 2446 | young 2447 | youtube 2448 | zac 2449 | zealand 2450 | zen 2451 | zone 2452 | -------------------------------------------------------------------------------- /models/TSC.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | 5 | # Topic-Semantic Contrastive Learning 6 | class TSC(nn.Module): 7 | def __init__(self, use_aug, temperature=0.07, weight_contrast=None): 8 | super().__init__() 9 | self.use_aug = use_aug 10 | self.temperature = temperature 11 | self.weight_contrast = weight_contrast 12 | 13 | print("===>Info: weight_contrast: ", self.weight_contrast) 14 | 15 | def forward(self, features, quant_idx=None, weight_same_quant=None): 16 | device = features.device 17 | 18 | batch_size = features.shape[0] 19 | mask = torch.eye(batch_size, dtype=torch.float32).to(device) 20 | 21 | contrast_count = features.shape[1] 22 | contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) 23 | anchor_feature = contrast_feature 24 | anchor_count = contrast_count 25 | 26 | anchor_dot_contrast = torch.div( 27 | torch.matmul(anchor_feature, contrast_feature.T), 28 | self.temperature 29 | ) 30 | 31 | # for numerical stability 32 | logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) 33 | logits = anchor_dot_contrast - logits_max.detach() 34 | 35 | # tile mask 36 | mask = mask.repeat(anchor_count, contrast_count) 37 | # mask-out self-contrast cases. 38 | # logits_mask is 1 - eye matrix 39 | logits_mask = torch.scatter( 40 | torch.ones_like(mask), 41 | 1, 42 | torch.arange(batch_size * anchor_count).view(-1, 1).to(device), 43 | 0 44 | ) 45 | 46 | mask = mask * logits_mask 47 | 48 | t_quant_idx = quant_idx.contiguous().view(-1, 1) 49 | 50 | # quant_idx_mask: 1 means same quantization; 0 means different quantization 51 | quant_idx_mask = torch.eq(t_quant_idx, t_quant_idx.T).float() 52 | quant_idx_mask = quant_idx_mask.repeat(anchor_count, contrast_count) 53 | 54 | exp_logits = torch.exp(logits) * (1 - quant_idx_mask) 55 | sum_exp_logits = exp_logits.sum(1, keepdim=True) 56 | 57 | if not self.use_aug: 58 | # quant_idx_mask includes self-contrast cases. 59 | # logits * logits_mask is to remove the positive pair but keep the negative pair in the self-contrast cases. 60 | # This is because some samples do not have positive pairs. 61 | log_prob = logits * logits_mask - torch.log(sum_exp_logits + 1e-10) 62 | mean_log_prob_pos = (quant_idx_mask * log_prob).sum(1) / quant_idx_mask.sum(1) 63 | else: 64 | log_prob = logits - torch.log(sum_exp_logits + 1e-10) 65 | # between original and augmented samples. 66 | mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) 67 | 68 | # between original samples. 69 | same_quant_mask = quant_idx_mask * logits_mask 70 | same_quant_mean_log_prob_pos = (same_quant_mask * log_prob).sum(1) / (same_quant_mask.sum(1) + 1e-10) 71 | mean_log_prob_pos += weight_same_quant * same_quant_mean_log_prob_pos 72 | 73 | loss = - self.weight_contrast * mean_log_prob_pos 74 | loss = loss.view(anchor_count, batch_size).sum(axis=0).mean() 75 | 76 | return loss 77 | -------------------------------------------------------------------------------- /models/TSCTM.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from models.TopicDistQuant import TopicDistQuant 5 | from models.TSC import TSC 6 | 7 | 8 | class TSCTM(nn.Module): 9 | def __init__(self, config): 10 | super().__init__() 11 | 12 | self.config = config 13 | 14 | hidden_dim = config.en1_units 15 | self.fc11 = nn.Linear(config.vocab_size, hidden_dim) 16 | self.fc12 = nn.Linear(hidden_dim, hidden_dim) 17 | self.fc21 = nn.Linear(hidden_dim, config.num_topic) 18 | 19 | self.mean_bn = nn.BatchNorm1d(config.num_topic) 20 | self.mean_bn.weight.requires_grad = False 21 | 22 | self.decoder_bn = nn.BatchNorm1d(config.vocab_size) 23 | self.decoder_bn.weight.requires_grad = False 24 | 25 | self.fcd1 = nn.Linear(config.num_topic, config.vocab_size, bias=False) 26 | 27 | for m in self.modules(): 28 | if isinstance(m, (nn.Conv2d, nn.Linear)): 29 | nn.init.xavier_uniform_(m.weight) 30 | if m.bias is not None: 31 | nn.init.zeros_(m.bias) 32 | 33 | self.topic_dist_quant = TopicDistQuant(config.num_topic, config.num_topic) 34 | self.contrast_loss = TSC(config.use_aug, temperature=config.temperature, weight_contrast=config.weight_contrast) 35 | 36 | def get_beta(self): 37 | return self.fcd1.weight.T 38 | 39 | def encode(self, inputs): 40 | e1 = F.softplus(self.fc11(inputs)) 41 | e1 = F.softplus(self.fc12(e1)) 42 | return self.mean_bn(self.fc21(e1)) 43 | 44 | def decode(self, theta): 45 | d1 = F.softmax(self.decoder_bn(self.fcd1(theta)), dim=1) 46 | return d1 47 | 48 | def get_theta(self, inputs): 49 | theta = self.encode(inputs) 50 | softmax_theta = F.softmax(theta, dim=1) 51 | return softmax_theta 52 | 53 | def forward(self, inputs): 54 | if self.config.use_aug: 55 | return self.forward_aug(inputs) 56 | else: 57 | return self.forward_noaug(inputs) 58 | 59 | def forward_noaug(self, inputs): 60 | theta = self.encode(inputs) 61 | softmax_theta = F.softmax(theta, dim=1) 62 | 63 | quant_rst = self.topic_dist_quant(softmax_theta) 64 | 65 | recon = self.decode(quant_rst['quantized']) 66 | loss = self.loss_function(recon, inputs) + quant_rst['loss'] 67 | 68 | features = torch.cat([F.normalize(theta, dim=1).unsqueeze(1)], dim=1) 69 | contrastive_loss = self.contrast_loss(features, quant_idx=quant_rst['encoding_indices']) 70 | loss += contrastive_loss 71 | 72 | return softmax_theta, loss, contrastive_loss 73 | 74 | def forward_aug(self, inputs): 75 | theta = self.encode(inputs[0]) 76 | softmax_theta = F.softmax(theta, dim=1) 77 | 78 | quant_rst = self.topic_dist_quant(softmax_theta) 79 | 80 | recon = self.decode(quant_rst['quantized']) 81 | loss = self.loss_function(recon, inputs[0]) + quant_rst['loss'] 82 | 83 | contrast_feature_list = list() 84 | for x in inputs[1:]: 85 | theta0 = self.encode(x) 86 | contrast_feature_list.append(theta0) 87 | 88 | softmax_theta0 = F.softmax(theta0, dim=1) 89 | quant_rst0 = self.topic_dist_quant(softmax_theta0) 90 | loss += quant_rst0['loss'] 91 | loss += self.loss_function(self.decode(quant_rst0['quantized']), x) 92 | 93 | loss /= len(inputs) 94 | 95 | contrast_feature = torch.cat([F.normalize(f, dim=1).unsqueeze(1) for f in contrast_feature_list], dim=1) 96 | features = torch.cat([F.normalize(theta, dim=1).unsqueeze(1), contrast_feature], dim=1) 97 | 98 | contrastive_loss = self.contrast_loss(features, quant_idx=quant_rst['encoding_indices'], weight_same_quant=self.config.weight_same_quant) 99 | 100 | loss += contrastive_loss 101 | 102 | return softmax_theta, loss, contrastive_loss 103 | 104 | def loss_function(self, recon_x, x): 105 | loss = -(x * (recon_x).log()).sum(axis=1) 106 | loss = loss.mean() 107 | return loss 108 | -------------------------------------------------------------------------------- /models/TopicDistQuant.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | 6 | class TopicDistQuant(nn.Module): 7 | def __init__(self, num_embeddings, embedding_dim, commitment_cost=0.1): 8 | super().__init__() 9 | 10 | self._embedding_dim = embedding_dim 11 | self._num_embeddings = num_embeddings 12 | self._embedding = nn.Embedding(self._num_embeddings, self._embedding_dim) 13 | self._embedding.weight.data.copy_(torch.eye(embedding_dim)) 14 | self._commitment_cost = commitment_cost 15 | 16 | def forward(self, inputs): 17 | # Calculate distances 18 | # NOTE: Do not use torch.cdist. It has unknown bugs. 19 | distances = (torch.sum(inputs**2, dim=1, keepdim=True) 20 | + torch.sum(self._embedding.weight**2, dim=1) 21 | - 2 * torch.matmul(inputs, self._embedding.weight.t())) 22 | 23 | # Encoding 24 | encoding_indices = torch.argmin(distances, dim=1) 25 | 26 | # Quantize and unflatten 27 | quantized = self._embedding(encoding_indices) 28 | 29 | # Loss 30 | e_latent_loss = F.mse_loss(quantized.detach(), inputs, reduction='none').sum(axis=1).mean() 31 | q_latent_loss = F.mse_loss(quantized, inputs.detach(), reduction='none').sum(axis=1).mean() 32 | loss = q_latent_loss + self._commitment_cost * e_latent_loss 33 | 34 | quantized = inputs + (quantized - inputs).detach() 35 | 36 | rst = { 37 | 'loss': loss, 38 | 'quantized': quantized, 39 | 'encoding_indices': encoding_indices, 40 | } 41 | 42 | return rst 43 | -------------------------------------------------------------------------------- /run.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | import scipy 4 | import torch 5 | import yaml 6 | from torch.utils.data import DataLoader 7 | from utils import data_utils 8 | from utils.Data import TextData 9 | from runners.Runner import Runner 10 | 11 | 12 | def parse_args(): 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument('--model', type=str, required=True) 15 | parser.add_argument('--data_dir', type=str, required=True) 16 | parser.add_argument('--num_epoch', type=int, default=200) 17 | parser.add_argument('--batch_size', type=int, default=200) 18 | parser.add_argument('--learning_rate', type=float, default=0.002) 19 | parser.add_argument('--num_topic', type=int, default=50) 20 | parser.add_argument('--en1_units', type=int, default=100) 21 | parser.add_argument('--num_top_word', type=int, default=15) 22 | parser.add_argument('--test_index', type=int, default=1) 23 | 24 | args = parser.parse_args() 25 | return args 26 | 27 | 28 | def save_theta(model_runner, train_dataset, use_aug, output_prefix): 29 | 30 | train_theta = model_runner.test(train_dataset.train_bow) 31 | scipy.sparse.save_npz('{}_theta_train.npz'.format(output_prefix), scipy.sparse.csr_matrix(train_theta)) 32 | 33 | if use_aug: 34 | scipy.sparse.save_npz('{}_theta_train.npz'.format(output_prefix), scipy.sparse.csr_matrix(train_theta)) 35 | 36 | for i, bow in enumerate(train_dataset.contrast_bow_list): 37 | contrast_theta = model_runner.test(bow) 38 | scipy.sparse.save_npz('{}_theta_{}.npz'.format(output_prefix, train_dataset.aug_option_list[i]), scipy.sparse.csr_matrix(contrast_theta)) 39 | else: 40 | scipy.sparse.save_npz('{}_theta_train.npz'.format(output_prefix), scipy.sparse.csr_matrix(train_theta)) 41 | 42 | 43 | def main(): 44 | config = parse_args() 45 | data_utils.update_args(config, f"configs/{config.model}.yaml") 46 | print("===>Info: args: \n", yaml.dump(vars(config), default_flow_style=False)) 47 | 48 | aug_option_list = None 49 | if config.use_aug: 50 | aug_option_list = ['contextual0.3', 'wordnet0.3'] 51 | aug_option_list.sort() 52 | print("===>Info: aug_option_list: ", aug_option_list) 53 | 54 | device = 'cuda' if torch.cuda.is_available() else 'cpu' 55 | 56 | train_dataset = TextData(config.data_dir, device, aug_option_list, config.use_aug) 57 | train_loader = DataLoader(train_dataset, config.batch_size, shuffle=True) 58 | config.vocab_size = len(train_dataset.vocab) 59 | 60 | # Training 61 | model_runner = Runner(config, device) 62 | beta = model_runner.train(train_loader) 63 | 64 | # Save output 65 | dataset_name = os.path.basename(config.data_dir) 66 | output_prefix = f'output/{dataset_name}/{config.model}_K{config.num_topic}_{config.test_index}th' 67 | 68 | data_utils.make_dir(os.path.dirname(output_prefix)) 69 | topic_str_list = data_utils.print_topic_words(beta, train_dataset.vocab, config.num_top_word) 70 | data_utils.save_text(topic_str_list, f'{output_prefix}_T{config.num_top_word}') 71 | 72 | save_theta(model_runner, train_dataset, config.use_aug, output_prefix) 73 | scipy.sparse.save_npz(f'{output_prefix}_beta.npz', scipy.sparse.csr_matrix(beta)) 74 | 75 | 76 | if __name__ == '__main__': 77 | main() 78 | -------------------------------------------------------------------------------- /runners/Runner.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | from models.TSCTM import TSCTM 4 | 5 | 6 | class Runner(): 7 | def __init__(self, config, device): 8 | self.config = config 9 | self.model = TSCTM(config) 10 | self.model = self.model.to(device) 11 | 12 | def train(self, train_loader): 13 | data_size = len(train_loader.dataset) 14 | optimizer = torch.optim.Adam(self.model.parameters(), self.config.learning_rate) 15 | 16 | for epoch in range(1, self.config.num_epoch + 1): 17 | self.model.train() 18 | 19 | train_loss = 0 20 | contrastive_loss = 0 21 | for idx, bows in enumerate(train_loader): 22 | 23 | _, batch_loss, batch_contrastive_loss = self.model(bows) 24 | 25 | optimizer.zero_grad() 26 | batch_loss.backward() 27 | optimizer.step() 28 | 29 | train_loss += batch_loss.cpu() * len(bows) 30 | contrastive_loss += batch_contrastive_loss.cpu() * len(bows) 31 | 32 | if epoch % 5 == 0: 33 | print('Epoch: {:03d}/{:03d} Loss: {:.3f}'.format(epoch, self.config.num_epoch, train_loss / data_size), end=' ') 34 | print('Contra_loss: {:.3f}'.format(contrastive_loss / data_size)) 35 | 36 | beta = self.model.get_beta().detach().cpu().numpy() 37 | 38 | return beta 39 | 40 | def test(self, inputs): 41 | data_size = inputs.shape[0] 42 | theta = list() 43 | all_idx = torch.split(torch.arange(data_size), self.config.batch_size) 44 | 45 | with torch.no_grad(): 46 | self.model.eval() 47 | for idx in all_idx: 48 | batch_inputs = inputs[idx] 49 | batch_theta = self.model.get_theta(batch_inputs) 50 | theta += list(batch_theta.detach().cpu().numpy()) 51 | 52 | theta = np.asarray(theta) 53 | return theta 54 | -------------------------------------------------------------------------------- /utils/Data.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | from sklearn.feature_extraction.text import CountVectorizer 4 | import torch 5 | from torch.utils.data import Dataset 6 | from utils import data_utils 7 | 8 | 9 | class TextData(Dataset): 10 | def __init__(self, data_dir, device, aug_option_list=None, use_aug=False): 11 | name = 'train_texts.txt' 12 | self.train_texts = data_utils.read_text(os.path.join(data_dir, '{}'.format(name))) 13 | train_size = len(self.train_texts) 14 | 15 | self.use_aug = use_aug 16 | self.aug_option_list = aug_option_list 17 | 18 | vectorizer = CountVectorizer() 19 | self.train_bow = vectorizer.fit_transform(self.train_texts).toarray().astype('float32') 20 | 21 | self.train_vocab = vectorizer.get_feature_names_out() 22 | 23 | if aug_option_list: 24 | print('===>Info: reading augmentation data...') 25 | 26 | self.num_contrast = len(aug_option_list) 27 | self.aug_texts_list = list() 28 | for aug_option in aug_option_list: 29 | aug_text_path = os.path.join(data_dir, '{}_{}'.format(name, aug_option)) 30 | print('===>reading {}'.format(aug_text_path)) 31 | self.aug_texts_list.append(data_utils.read_text(aug_text_path)) 32 | 33 | self.combined_train_texts = np.concatenate((np.asarray(self.train_texts), np.asarray(self.aug_texts_list).flatten())) 34 | combined_train_bow = vectorizer.fit_transform(self.combined_train_texts).toarray().astype('float32') 35 | 36 | if self.use_aug: 37 | self.train_bow = combined_train_bow[:train_size] 38 | self.contrast_bow_list = np.array_split(combined_train_bow[train_size:], self.num_contrast) 39 | self.contrast_bow_list = [torch.tensor(bow).to(device) for bow in self.contrast_bow_list] 40 | 41 | else: 42 | self.train_bow = combined_train_bow 43 | 44 | self.train_bow = torch.tensor(self.train_bow).to(device) 45 | self.vocab = vectorizer.get_feature_names_out() 46 | 47 | def __len__(self): 48 | return len(self.train_bow) 49 | 50 | def __getitem__(self, idx): 51 | if self.use_aug: 52 | return [self.train_bow[idx]] + [bow[idx] for bow in self.contrast_bow_list] 53 | else: 54 | return self.train_bow[idx] 55 | -------------------------------------------------------------------------------- /utils/TU.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | import numpy as np 4 | import data_utils 5 | from sklearn.feature_extraction.text import CountVectorizer 6 | 7 | 8 | def parse_args(): 9 | parser = argparse.ArgumentParser() 10 | parser.add_argument('--data_path') 11 | parser.add_argument('--verbose', action='store_true', default=False) 12 | args = parser.parse_args() 13 | return args 14 | 15 | 16 | def TU_eva(texts, verbose=False): 17 | K = len(texts) 18 | T = len(texts[0].split()) 19 | vectorizer = CountVectorizer() 20 | counter = vectorizer.fit_transform(texts).toarray() 21 | 22 | TU = 0.0 23 | TF = counter.sum(axis=0) 24 | cnt = TF * (counter > 0) 25 | 26 | # # output most frequent words 27 | if verbose: 28 | word_index_dict = dict(zip(vectorizer.vocabulary_.values(), vectorizer.vocabulary_.keys())) 29 | for index in TF.argsort()[-20:][::-1]: 30 | print('{:10s}: {}'.format(word_index_dict[index], TF[index])) 31 | print() 32 | 33 | for i in range(K): 34 | TU += (1 / cnt[i][np.where(cnt[i] > 0)]).sum() / T 35 | TU /= K 36 | 37 | return TU 38 | 39 | 40 | if __name__ == "__main__": 41 | args = parse_args() 42 | texts = data_utils.read_text(args.data_path) 43 | TU = TU_eva(texts, verbose=args.verbose) 44 | print("===>TU: {:5f}".format(TU)) 45 | -------------------------------------------------------------------------------- /utils/data_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | import yaml 4 | import numpy as np 5 | 6 | 7 | def print_topic_words(beta, vocab, num_top_word=15): 8 | 9 | topic_str_list = [] 10 | for i, topic_dist in enumerate(beta): 11 | topic_words = np.asarray(vocab)[np.argsort(topic_dist)][:-(num_top_word + 1):-1] 12 | topic_str = ' '.join(topic_words) 13 | topic_str_list.append(topic_str) 14 | # print('Topic {}: {}'.format(i + 1, topic_str)) 15 | return topic_str_list 16 | 17 | 18 | def update_args(args, path): 19 | if not os.path.exists(path): 20 | raise FileNotFoundError(path) 21 | 22 | with open(path, encoding='utf-8') as file: 23 | config = yaml.safe_load(file) 24 | if config: 25 | args = vars(args) 26 | args.update(config) 27 | args = argparse.Namespace(**args) 28 | print("===>Info: use setting in file {}.".format(path)) 29 | 30 | 31 | def make_dir(path): 32 | os.makedirs(path, exist_ok=True) 33 | 34 | 35 | def read_text(path): 36 | texts = list() 37 | with open(path, 'r', encoding='utf-8') as file: 38 | for line in file: 39 | texts.append(line.strip()) 40 | return texts 41 | 42 | 43 | def save_text(texts, path): 44 | with open(path, 'w', encoding='utf-8') as file: 45 | for text in texts: 46 | file.write(text.strip() + '\n') 47 | 48 | 49 | def split_text_word(texts): 50 | texts = [text.split() for text in texts] 51 | return texts 52 | --------------------------------------------------------------------------------