├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Haofei Yu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Personalized Text Generation 2 | 3 | Personalized text generation can be considered as a sub-topic under knowledge-grounded text generation and controllabel text generation. 4 | 5 | Personalized text generation includes topics like : 6 | 7 | 1. personalized dialogue generation 8 | 2. personalized machine translation 9 | 3. personalized summarization 10 | 4. personalized review generation 11 | 5. personalized QA generation 12 | 6. personalized product descritpion generation / recipe generation and other generation topic 13 | 14 | For each paper, we pay attention to four themes mentioned in these papers: 15 | 16 | 1. how to do personalized train/inference 17 | 2. how to model personality 18 | 3. which dataset do they use 19 | 4. how to evaluate personalized performance 20 | 21 | As a result, we organize the paperlist based on their generation task and classify them according to answer the four questions. 22 | 23 | This paperlist not only includes papers aiming at solving personalized problems, but also contains papers including techniques suitable for personalized text generation. 24 | 25 | 26 | 27 | 28 | 29 | ## Part1 List and classify representative papers on personalized text generation 30 | 31 | Papers in part1 are listed according to answer the four questions mentioned above. 32 | 33 | 1. Personalized Methods 34 | 35 | 1. personalized model architecture 36 | 37 | 1. Attention Fusion 38 | 39 | [arxiv2019] [Personalized Dialogue Generation with Diversified Traits](https://arxiv.org/pdf/1901.09672.pdf) 40 | 41 | [AAAI2020] [A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data](https://arxiv.org/pdf/1911.04700.pdf) 42 | 43 | [arxiv2021] [Bilateral Personalized Dialogue Generation with Dynamic Persona-Aware Fusion](https://arxiv.org/pdf/2106.07857.pdf) 44 | 45 | 2. Memory Network 46 | 47 | [EMNLP2018] [Training Millions of Personalized Dialogue Agents](https://aclanthology.org/D18-1298.pdf) 48 | 49 | [NAACL2021] [Personalized Response Generation via Generative Split Memory Network](https://aclanthology.org/2021.naacl-main.157.pdf) 50 | 51 | 3. Pointer Network 52 | 53 | [SIGDAIL2019] [DEEPCOPY: Grounded Response Generation with Hierarchical Pointer Networks](http://aclanthology.lst.uni-saarland.de/W19-5917.pdf) 54 | 55 | 4. AutoEncoder-based 56 | 57 | [EMNLP2019] [Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders](https://aclanthology.org/D19-1201.pdf) 58 | 59 | [IJCAI2019] [Exploiting Persona Information for Diverse Generation of Conversational Responses](https://www.ijcai.org/proceedings/2019/0721.pdf) 60 | 61 | [ACL2020] [Guiding Variational Response Generator to Exploit Persona](https://aclanthology.org/2020.acl-main.7.pdf) 62 | 63 | 5. Adapter-based 64 | 65 | [AAAI2021] [The Adapter-Bot: All-In-One Controllable Conversational Model](https://www.aaai.org/AAAI21Papers/DEMO-233.LinZ.pdf) 66 | 67 | 2. personalized learning and inference 68 | 69 | 1. multi-task learning 70 | 71 | [IJCNLP2017] [Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models](http://aclanthology.lst.uni-saarland.de/I17-1061.pdf) 72 | 73 | [EMNLP2020] [A Multi-Persona Chatbot for Hotline Counselor Training](https://aclanthology.org/2020.findings-emnlp.324.pdf) 74 | 75 | 2. interpolation personalized model and global model 76 | 77 | [NLI2020] [Examination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation](https://aclanthology.org/2020.nli-1.3.pdf) 78 | 79 | 3. domain adaptation on global model 80 | 81 | [SIGIR2017] [Personalized Response Generation via Domain adaptation](https://dl.acm.org/doi/10.1145/3077136.3080706) 82 | 83 | [AAAI2021] [The Adapter-Bot: All-In-One Controllable Conversational Model](https://www.aaai.org/AAAI21Papers/DEMO-233.LinZ.pdf) 84 | 85 | [arxiv2021] [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/pdf/2101.00190.pdf) 86 | 87 | 4. consistency augmented 88 | 89 | [ACL2019] [Dialogue Natural Language Inference](https://aclanthology.org/P19-1363.pdf) 90 | 91 | [AAAI2020] [Generating Persona Consistent Dialogues by Exploiting Natural Language Inference](https://arxiv.org/pdf/1911.05889.pdf) 92 | 93 | 5. Rational Speech Acts-based 94 | 95 | 2. Personality Modeling 96 | 97 | 1. Model personality in continious space 98 | 99 | 1. independent user embedding 100 | 101 | [ACL2016] [A Persona-Based Neural Conversation Model](https://aclanthology.org/P16-1094.pdf) 102 | 103 | [IJCAI2017] [Exploring Personalized Neural Conversational Models](https://www.ijcai.org/proceedings/2017/0521.pdf) 104 | 105 | [ACL2020] [Guiding Variational Response Generator to Exploit Persona](https://aclanthology.org/2020.acl-main.7.pdf) 106 | 107 | [GEMworkshop2021] [Personalized Response Generation with Tensor Factorization](https://aclanthology.org/2021.gem-1.5.pdf) 108 | 109 | 2. personal linguistic style extracted from annotated utterance 110 | 111 | [arxiv2019] [Consistent Dialogue Generation with Self-supervised Feature Learning](https://arxiv.org/pdf/1903.05759.pdf) 112 | 113 | [EMNLP2020] [A Multi-Persona Chatbot for Hotline Counselor Training](https://aclanthology.org/2020.findings-emnlp.324.pdf) 114 | 115 | [ACL2020] [Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks](https://aclanthology.org/2020.acl-main.517.pdf) 116 | 117 | 2. Model personality in a concrete media 118 | 119 | 1. raw text profile 120 | 121 | [ACL2018] [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://aclanthology.org/P18-1205.pdf) 122 | 123 | 2. key-value profile 124 | 125 | [IJCAI2018] [Assigning Personality/Identity to a Chatting Machine for Coherent Conversation Generation](https://www.ijcai.org/proceedings/2018/0595.pdf) 126 | 127 | 3. expanded text profile 128 | 129 | [EMNLP2020] [Like hiking? You probably *enjoy nature*: Persona-grounded Dialog with Commonsense Expansions](https://arxiv.org/pdf/2010.03205.pdf) 130 | 131 | [EMNLP2020] [Toward Stance-based Personas for Opinionated Dialogues](https://aclanthology.org/2020.findings-emnlp.238.pdf) 132 | 133 | [ACL2021] [Unsupervised Enrichment of Persona-grounded Dialog with Background Stories](https://arxiv.org/pdf/2106.08364.pdf) 134 | 135 | 3. Model personality in an interactive action 136 | 137 | 1. Reinforcement Learning interaction 138 | 139 | [AAAI2018] [Personalizing a Dialogue System with Transfer Reinforcement Learning](https://arxiv.org/pdf/1610.02891.pdf) 140 | 141 | [ACL2021] [Improving Dialog Systems for Negotiation with Personality Modeling](https://aclanthology.org/2021.acl-long.56.pdf) 142 | 143 | 2. feedback from users 144 | 145 | [ACL2017] [Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback](https://aclanthology.org/P17-1124.pdf) 146 | 147 | [PBML2017] [Continuous Learning from Human Post-Edits for Neural Machine Translation](https://core.ac.uk/download/pdf/226078161.pdf) 148 | 149 | 3. Personalized Dataset 150 | 151 | 1. contains text with annotated persona attributes 152 | 153 | Twitter / Reddit / Opensubtitles / Malluba 154 | 155 | 2. contains text with persona profile 156 | 157 | PERSONACHAT 158 | 159 | 4. Personalized Evaluation 160 | 1. Automatic Evaluation 161 | 162 | PPL / BLEU / ROUGE-L / METEOR / Distinct / Entropy / Consistency score / word-embedding level metric 163 | 164 | 2. Human Evaluation 165 | 166 | consistency / fluency / informativeness / relevance / appropriateness / agreement / fact-inclusion 167 | 168 | cohen's k score 169 | 170 | 171 | 172 | ## Part2 List all related or inspiring papers regarding to personalized text generation 173 | 174 | Papers in part2 are listed according to their generation task instead of methods or dataset. Papers might not be directly related to personalized text generation but at least, from my perspective, can provide some interesing insight into this topic. 175 | 176 | 1. Related survey and PhD thesis 177 | 178 | [PhD Thesis] [Controllable Text Generation](https://www.lti.cs.cmu.edu/sites/default/files/prabhomoye%2C%20shrimai%20-%20CMU-LTI-21-001.pdf) 179 | 180 | [PhD Thesis] [Neural Generation of open-ended text and dialogue](https://stacks.stanford.edu/file/druid:hw190jq4736/AbigailSeeThesis-augmented.pdf) 181 | 182 | [survey] [Pretrained Language Models for Text Generation: A Survey](https://arxiv.org/pdf/2105.10311.pdf) 183 | 184 | [survey] [Neural Approaches to Conversational AI](https://dl.acm.org/doi/abs/10.1145/3209978.3210183) 185 | 186 | [survey] [A Survey of Knowledge-Enhanced Text Generation](https://arxiv.org/pdf/2010.04389.pdf) 187 | 188 | 189 | 190 | 2. Personalized Dialog Generation 191 | 192 | [ACL2007] [PERSONAGE: Personality Generation for Dialogue](https://aclanthology.org/P07-1063.pdf) 193 | 194 | [ACL2012] [Personalized Normalization for a Multilingual Chat System](https://aclanthology.org/P12-3006.pdf) 195 | 196 | [ICML2015] [A Neural Conversational Model](https://arxiv.org/pdf/1506.05869.pdf) 197 | 198 | [SIGDAIL2016] [Analyzing Post-dialogue Comments by Speakers – How Do Humans Personalize Their Utterances in Dialogue? –](https://aclanthology.org/W16-3619.pdf) 199 | 200 | [arxiv2016] [Conversational Contextual Cues: The Case of Personalization and History for Response Ranking](https://arxiv.org/pdf/1606.00372.pdf) 201 | 202 | **[ACL2016]** [A Persona-Based Neural Conversation Model](https://aclanthology.org/P16-1094.pdf) 203 | 204 | [arxiv2017] [Neural Personalized Response Generation as Domain Adaptation](https://arxiv.org/pdf/1701.02073.pdf) 205 | 206 | [IJCAI2017] [Exploring Personalized Neural Conversational Models](https://www.ijcai.org/proceedings/2017/0521.pdf) 207 | 208 | [NIPS2017] [Personalization in Goal-oriented Dialog](https://arxiv.org/pdf/1706.07503.pdf) 209 | 210 | [INLG2017] [Neural Response Generation for Customer Service based on Personality Traits](https://aclanthology.org/W17-3541.pdf) 211 | 212 | [IJCNLP2017] [Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models](http://aclanthology.lst.uni-saarland.de/I17-1061.pdf) 213 | 214 | [SIGIR2017] [Personalized Response Generation via Domain adaptation](https://dl.acm.org/doi/10.1145/3077136.3080706) 215 | 216 | [EMNLP2017] [Steering Output Style and Topic in Neural Response Generation](https://aclanthology.org/D17-1228.pdf) 217 | 218 | [AAAI2018] [Personalizing a Dialogue System with Transfer Reinforcement Learning](https://arxiv.org/pdf/1610.02891.pdf) 219 | 220 | [EMNLP2018] [Learning Personas from Dialogue with Attentive Memory Networks](https://aclanthology.org/D18-1284.pdf) 221 | 222 | [EMNLP2018] [Training Millions of Personalized Dialogue Agents](https://aclanthology.org/D18-1298.pdf) 223 | 224 | [EMNLPwokshop2018] [Retrieve and Refine: Improved Sequence Generation Models For Dialogue](https://aclanthology.org/W18-5713.pdf) 225 | 226 | [SIAM2018] [Investigating Deep Reinforcement Learning Techniques in Personalized Dialogue Generation](https://epubs.siam.org/doi/abs/10.1137/1.9781611975321.71?mobileUi=0) 227 | 228 | [IJCAI2018] [Assigning Personality/Identity to a Chatting Machine for Coherent Conversation Generation](https://www.ijcai.org/proceedings/2018/0595.pdf) 229 | 230 | [LREC2018] [Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models](https://aclanthology.org/L18-1496.pdf) 231 | 232 | **[ACL2018]** [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://aclanthology.org/P18-1205.pdf) 233 | 234 | [EMNLP2019] [Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots](https://aclanthology.org/D19-1193.pdf) 235 | 236 | [EMNLP2019] [Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders](https://aclanthology.org/D19-1201.pdf) 237 | 238 | [EMNLP2019] [Structuring Latent Spaces for Stylized Response Generation](https://aclanthology.org/D19-1190.pdf) 239 | 240 | [EMNLP2019] [Variational Hierarchical User-based Conversation Model](https://aclanthology.org/D19-1202.pdf) 241 | 242 | [SIGDAIL2019] [DEEPCOPY: Grounded Response Generation with Hierarchical Pointer Networks](http://aclanthology.lst.uni-saarland.de/W19-5917.pdf) 243 | 244 | [AAAI2019] [Learning Personalized End-to-End Goal-Oriented Dialog](https://arxiv.org/pdf/1811.04604.pdf) 245 | 246 | [NIPSworkshop2019] [Persona-aware Dialogue Generation with Enriched Profile](https://arxiv.org/pdf/1901.09672.pdf) 247 | 248 | [IJCAI2019] [Exploiting Persona Information for Diverse Generation of Conversational Responses](https://www.ijcai.org/proceedings/2019/0721.pdf) 249 | 250 | [NAACL2019] [An Adversarial Learning Framework For A Persona-Based Multi-Turn Dialogue Model](https://aclanthology.org/W19-2301.pdf) 251 | 252 | **[ACL2019]** [Dialogue Natural Language Inference](https://aclanthology.org/P19-1363.pdf) 253 | 254 | [ACL2019] [Large-Scale Transfer Learning for Natural Language Generation](https://aclanthology.org/P19-1608.pdf) 255 | 256 | [ACL2019] [Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment](https://aclanthology.org/P19-1535.pdf) 257 | 258 | [ACL2019] [Personalizing Dialogue Agents via Meta-Learning](https://aclanthology.org/P19-1542.pdf) 259 | 260 | [ACL2019] [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://aclanthology.org/P19-1566.pdf) 261 | 262 | [arxiv2019] [Personalized Dialogue Generation with Diversified Traits](https://arxiv.org/pdf/1901.09672.pdf) 263 | 264 | [arxiv2019] [Consistent Dialogue Generation with Self-supervised Feature Learning](https://arxiv.org/pdf/1903.05759.pdf) 265 | 266 | [arxiv2019] [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://arxiv.org/pdf/1901.08149.pdf) 267 | 268 | [AAAI2020] [Generating Persona Consistent Dialogues by Exploiting Natural Language Inference](https://arxiv.org/pdf/1911.05889.pdf) 269 | 270 | [AAAI2020] [A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data](https://arxiv.org/pdf/1911.04700.pdf) 271 | 272 | [ACL2020] [Don’t Say *That*! Making Inconsistent Dialogue Unlikely with Unlikelihood Training](https://aclanthology.org/2020.acl-main.428.pdf) 273 | 274 | [ACL2020] [Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation](https://aclanthology.org/2020.acl-main.516.pdf) 275 | 276 | [ACL2020] [Guiding Variational Response Generator to Exploit Persona](https://aclanthology.org/2020.acl-main.7.pdf) 277 | 278 | [ACL2020] [Improving Disentangled Text Representation Learning with Information-Theoretic Guidance](https://aclanthology.org/2020.acl-main.673.pdf) 279 | 280 | [ACL2020] [Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks](https://aclanthology.org/2020.acl-main.517.pdf) 281 | 282 | [ACL2020] [You Impress Me: Dialogue Generation via Mutual Persona Perception](https://aclanthology.org/2020.acl-main.131.pdf) 283 | 284 | [ACLworkshop2020] [Sketch-Fill-A-R: A Persona-Grounded Chit-Chat Generation Framework](https://aclanthology.org/2020.nlp4convai-1.14.pdf) 285 | 286 | [EMNLP2020] [A Multi-Persona Chatbot for Hotline Counselor Training](https://aclanthology.org/2020.findings-emnlp.324.pdf) 287 | 288 | [EMNLP2020] [Like hiking? You probably *enjoy nature*: Persona-grounded Dialog with Commonsense Expansions](https://arxiv.org/pdf/2010.03205.pdf) 289 | 290 | [EMNLP2020] [Profile Consistency Identification for Open-domain Dialogue Agents](https://aclanthology.org/2020.emnlp-main.539.pdf) 291 | 292 | [EMNLP2020] [Toward Stance-based Personas for Opinionated Dialogues](https://aclanthology.org/2020.findings-emnlp.238.pdf) 293 | 294 | [EMNLP2020] [Towards Persona-Based Empathetic Conversational Models](https://aclanthology.org/2020.emnlp-main.531.pdf) 295 | 296 | [EMNLP2020] [Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness](https://aclanthology.org/2020.emnlp-main.65.pdf) 297 | 298 | [ECAI2020] [A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation](https://ecai2020.eu/papers/345_paper.pdf) 299 | 300 | [arxiv2020] [Recipes for building an open-domain chatbot](https://arxiv.org/pdf/2004.13637.pdf) 301 | 302 | [arxiv2020] [Towards a Human-like Open-Domain Chatbot](https://arxiv.org/pdf/2001.09977.pdf) 303 | 304 | [GEMworkshop2021] [Personalized Response Generation with Tensor Factorization](https://aclanthology.org/2021.gem-1.5.pdf) 305 | 306 | [CIKM2021] [Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot](https://arxiv.org/pdf/2108.07935.pdf) 307 | 308 | [ACL2021] [BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data](https://aclanthology.org/2021.acl-long.14.pdf) 309 | 310 | [ACL2021] [Improving Dialog Systems for Negotiation with Personality Modeling](https://aclanthology.org/2021.acl-long.56.pdf) 311 | 312 | [ACL2021] [Unsupervised Enrichment of Persona-grounded Dialog with Background Stories](https://arxiv.org/pdf/2106.08364.pdf) 313 | 314 | [AAAI2021] [The Adapter-Bot: All-In-One Controllable Conversational Model](https://www.aaai.org/AAAI21Papers/DEMO-233.LinZ.pdf) 315 | 316 | [NNLS2021] [Multitask Learning and Reinforcement Learning for Personalized Dialog Generation: An Empirical Study](https://ieeexplore.ieee.org/document/9025776) 317 | 318 | [SIGIR2021] [One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles](https://arxiv.org/pdf/2108.09355.pdf) 319 | 320 | [NAACL2021] [Personalized Response Generation via Generative Split Memory Network](https://aclanthology.org/2021.naacl-main.157.pdf) 321 | 322 | [arxiv2021] [Bilateral Personalized Dialogue Generation with Dynamic Persona-Aware Fusion](https://arxiv.org/pdf/2106.07857.pdf) 323 | 324 | [arxiv2021] [Content Selection Network for Document-grounded Retrieval-based Chatbots](https://arxiv.org/pdf/2101.08426.pdf) 325 | 326 | [EMNLP2021] [Detecting Speaker Personas from Conversational Texts](https://arxiv.org/pdf/2109.01330.pdf) 327 | 328 | 329 | 330 | 3. Personalized Summarization 331 | 332 | [AH2008] [Aspect-Based Personalized Text Summarization](https://link.springer.com/chapter/10.1007/978-3-540-70987-9_31) 333 | 334 | [EMNLP2011] [Summarize What You Are Interested In: An Optimization Framework for Interactive Personalized Summarization](https://aclanthology.org/D11-1124.pdf) 335 | 336 | [COLING2012] [Context­Enhanced Personalized Social Summarization](https://aclanthology.org/C12-1075.pdf) 337 | 338 | [INLG2014] [Adapting graph summaries to the users reading levels](https://aclanthology.org/W14-4409.pdf) 339 | 340 | [ACL2017] [Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback](https://aclanthology.org/P17-1124.pdf) 341 | 342 | [NAACL2018] [Towards Generating Personalized Hospitalization Summaries](https://aclanthology.org/N18-4011.pdf) 343 | 344 | [arxiv2020] [Adaptive Summaries: A Personalized Concept-based Summarization Approach by Learning from Users’ Feedback](https://arxiv.org/abs/2012.13387) 345 | 346 | 347 | 348 | 4. Personalized Machine Translation 349 | 350 | [IJCNLP2008] [Statistical Machine Translation Models for Personalized Search](https://aclanthology.org/I08-1068.pdf) 351 | 352 | [EACL2014] [Learning from Post-Editing: Online Model Adaptation for Statistical Machine Translation](https://aclanthology.org/E14-1042.pdf) 353 | 354 | [EMNLP2015] [Personalized Machine Translation: Predicting Translational Preferences](https://aclanthology.org/D15-1238.pdf) 355 | 356 | [EACL2017] [Personalized Machine Translation: Preserving Original Author Traits](https://aclanthology.org/E17-1101.pdf) 357 | 358 | [PBML2017] [Continuous Learning from Human Post-Edits for Neural Machine Translation](https://core.ac.uk/download/pdf/226078161.pdf) 359 | 360 | [EMNLP2018] [Compact Personalized Models for Neural Machine Translation](https://aclanthology.org/D18-1104.pdf) 361 | 362 | [ACL2018] [Extreme Adaptation for Personalized Neural Machine Translation](https://aclanthology.org/P18-2050.pdf) 363 | 364 | [ALTA2018] [Improved Neural Machine Translation using Side Information](https://aclanthology.org/U18-1001.pdf) 365 | 366 | [EMNLP2019] [Simple, Scalable Adaptation for Neural Machine Translation](https://aclanthology.org/D19-1165.pdf) 367 | 368 | [MTSummit2019] [Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation](https://aclanthology.org/W19-6610.pdf) 369 | 370 | [ACL2021] [Towards User-Driven Neural Machine Translation](https://aclanthology.org/2021.acl-long.310.pdf) 371 | 372 | 373 | 374 | 5. Personalized Review Generation 375 | 376 | [CIKM2014] [Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network](https://arxiv.org/pdf/2010.01480.pdf) 377 | 378 | [IJCNLP2017] [Estimating Reactions and Recommending Products with Generative Models of Reviews](https://aclanthology.org/I17-1079.pdf) 379 | 380 | [ACL2018] [Personalized Review Generation by Expanding Phrases and Attending on Aspect-Aware Representations](https://aclanthology.org/P18-2112.pdf) 381 | 382 | [ACLworkshop2019] [Automatic Generation of Personalized Comment Based on User Profile]() 383 | 384 | [WWW2019] [Persona-Aware Tips Generation](https://arxiv.org/abs/1903.02156) 385 | 386 | [EMNLP2019] [Towards controllable and personalized review generation](https://aclanthology.org/D19-1319.pdf) 387 | 388 | 389 | 390 | 6. Personalized Questions and Answering 391 | 392 | [COLING2008 workshop] [Personalized, Interactive Question Answering on the Web](https://aclanthology.org/W08-1605.pdf) 393 | 394 | [INLG2017] [Personalized Questions, Answers and Grammars: Aiding the Search for Relevant Web Information](https://aclanthology.org/W17-3530.pdf) 395 | 396 | 397 | 398 | 7. General Personalized Generation 399 | 400 | [CL2011] [Controlling user perceptions of linguistic style: Trainable generation of personality traits](https://aclanthology.org/J11-3002.pdf) 401 | 402 | [SLT2012] [Personalized language modeling by crowd sourcing with social network data for voice access of cloud applications](https://ieeexplore.ieee.org/document/6424220) 403 | 404 | [ACL2014] [Enriching Cold Start Personalized Language Model Using Social Network Information](https://aclanthology.org/P14-2100.pdf) 405 | 406 | [TASLP2016] [Personalizing Recurrent Neural Network Based Language Model by Social Network](https://dl.acm.org/doi/10.1109/TASLP.2016.2635445) 407 | 408 | [ACL2018] [Personalized Language Model for Query Auto-Completion](https://aclanthology.org/P18-2111.pdf) 409 | 410 | [EMNLP2019] [Generating Personalized Recipes from Historical User Preferences](https://arxiv.org/pdf/1909.00105.pdf) 411 | 412 | [KDD2019] [Towards Knowledge-Based Personalized Product Description Generation in E-commerce](https://arxiv.org/pdf/1903.12457.pdf) 413 | 414 | [ACLworkshop2019] [“My Way of Telling a Story”: Persona based Grounded Story Generation](https://arxiv.org/pdf/1906.06401.pdf) 415 | 416 | [NLI2020] [Examination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation](https://aclanthology.org/2020.nli-1.3.pdf) 417 | 418 | [COLING2020] [Personalized Multimodal Feedback Generation in Education](https://aclanthology.org/2020.coling-main.166.pdf) 419 | 420 | [WSDM2021] [Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph](https://dl.acm.org/doi/abs/10.1145/3437963.3441816) 421 | 422 | [ACL2021] [PENS: A Dataset and Generic Framework for Personalized News Headline Generation](https://aclanthology.org/2021.acl-long.7.pdf) 423 | 424 | [arxiv2021] [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/pdf/2101.00190.pdf) 425 | 426 | 427 | 428 | 8. Personalized Generation Evaluation 429 | 430 | [EMNLP2016] [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://arxiv.org/pdf/1603.08023.pdf) 431 | 432 | [LREC2020] [Evaluating Approaches to Personalizing Language Models](https://aclanthology.org/2020.lrec-1.299.pdf) 433 | 434 | [CORR2020] [XPersona: Evaluating Multilingual Personalized Chatbot](https://arxiv.org/pdf/2003.07568.pdf) 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | --------------------------------------------------------------------------------