├── README.md └── LICENSE /README.md: -------------------------------------------------------------------------------- 1 | # ModularQA 2 | ModularQA is a QA system that answers complex multi-hop and discrete reasoning questions by decomposing them into sub-questions answerable by two sub-models: a neural factoid single-span QA model and a symbolic calculator. These sub-questions and answers provided by the sub-model provide a natural language explanation of the 3 | model’s reasoning. This system is designed and trained based on the Text Modular Networks framework where the decompositions are generated in the language of the sub-models without needing annotated decompositions. For more details, refer to the [paper](https://www.semanticscholar.org/paper/0e1d82d24d58433ce9e211551605a0bfd296624f). 4 | 5 | 6 | 7 | ## Paper 8 | ``` 9 | Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models 10 | Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, Ashish Sabharwal 11 | NAACL 2021 12 | ``` 13 | Bibtex: 14 | ``` 15 | @inproceedings{WhatsMissing19, 16 | title={Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models}, 17 | author={Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, Ashish Sabharwal}, 18 | booktitle={NAACL}, 19 | year={2021} 20 | } 21 | ``` 22 | 23 | 24 | ## Demo 25 | [https://modularqa-demo.apps.allenai.org/](https://modularqa-demo.apps.allenai.org/) 26 | 27 | Note that responses might be slow 28 | 29 | ## Data 30 | 31 | ### QA Datasets 32 | We used the following subsets of HotpotQA and DROP to train and evaluate our models 33 | * [HotpotQA](https://ai2-public-datasets.s3.amazonaws.com/modularqa/hotpot_subset.zip) 34 | * [DROP](https://ai2-public-datasets.s3.amazonaws.com/modularqa/drop_subset.zip) 35 | * [DROP Evaluation Partitions](https://ai2-public-datasets.s3.amazonaws.com/modularqa/drop_partitions.zip) 36 | 37 | 38 | ### Training Datasets 39 | * [NextGen Training Data](https://ai2-public-datasets.s3.amazonaws.com/modularqa/modularqa_nextgen_train.zip): 40 | The decomposition chains generated from these DROP+HotpotQA subsets. These chains were used to train the NextGen model. 41 | 42 | * [Chains Scorer Training Data](https://ai2-public-datasets.s3.amazonaws.com/modularqa/chains_scorer_training.zip): 43 | The chains generated by running inference using our NextGen model with associated labels: 1 indicates the final answer produced by this chain is correct (F1>0.2) and 0 indicates incorrect. 44 | 45 | 46 | ### Predictions 47 | If you want the predictions of the system, without having to run the code, we provide them here: 48 | 49 | * [HotpotQA Predictions](https://ai2-public-datasets.s3.amazonaws.com/modularqa/hotpot_predictions.zip) 50 | * [DROP Predictions](https://ai2-public-datasets.s3.amazonaws.com/modularqa/drop_predictions.zip) 51 | 52 | 53 | ## Models 54 | We also provide the trained models used in our system. 55 | 56 | * [NextGen Model](https://ai2-public-datasets.s3.amazonaws.com/modularqa/nextgen_model.zip): 57 | A BART-Large model trained to produce the next sub-question given the complex question and previous question-answer pairs. Sample input-output: 58 | ``` 59 | Input: 60 | QC: When did the magazine Wallace Hester work for run? QI: (squad) What magazine did Hester work for? A: "Vanity Fair". QS: 61 | 62 | Output: 63 | (squad) When did the second Vanity Fair run? 64 | ``` 65 | 66 | * [Chains Scorer Model](https://ai2-public-datasets.s3.amazonaws.com/modularqa/chains_scorer.zip): 67 | A RoBERTa-Large model trained to predict whether the final answer produced by an inference chain is correct (captured by the score for the label 1). Sample input: 68 | ``` 69 | QC: How many percent of jobs were not in wholesale? QI: (squad)What percent of jobs are in wholesale? A: 12.4 QI: (math)not(12.4) A: 87.6 QS: [EOQ] 70 | ``` 71 | 72 | * [SQuAD QA Model](https://ai2-public-datasets.s3.amazonaws.com/modularqa/qa_model.zip): A RoBERTa-Large QA model trained on SQuAD 2.0. 73 | 74 | ## Code 75 | We use a fork of the [HuggingFace Transformers](https://github.com/huggingface/transformers) codebase: [https://github.com/tusharkhot/transformers/tree/modularqav2](https://github.com/tusharkhot/transformers/tree/modularqav2). This is based on an older version of the Transformers codebase. 76 | 77 | 78 | ## Running Inference 79 | To run inference, follow the following steps: 80 | 81 | 1. Clone the github repo from [https://github.com/tusharkhot/transformers](https://github.com/tusharkhot/transformers) and checkout the `modularqav2` branch. 82 | ``` 83 | git clone https://github.com/tusharkhot/transformers.git modularqa_transformers 84 | cd modularqa_transformers 85 | git checkout modularqav2 86 | export PYTHONPATH=src 87 | ``` 88 | 89 | 90 | 2. Download the HotpotQA and DROP subsets from above and unzip here 91 | ``` 92 | wget https://ai2-public-datasets.s3.amazonaws.com/modularqa/hotpot_subset.zip 93 | unzip hotpot_subset.zip 94 | 95 | wget https://ai2-public-datasets.s3.amazonaws.com/modularqa/drop_subset.zip 96 | unzip drop_subset.zip 97 | ``` 98 | 99 | 3. Save (or download from above) the trained 100 | * NextGen model to `nexgen_model/` 101 | * Chain scorer to `chain_scorer/` 102 | * SQuAD 2.0 QA model to `qa_model/` 103 | 104 | If the models are downloaded to a different path, change the paths in the config files below. 105 | 106 | 4. Run inference on HotpotQA Dev set, run: 107 | ``` 108 | python -u -m modularqa.inference.configurable_inference \ 109 | --input hotpot_subset/dev.json \ 110 | --output predictions_hotpot_dev.json \ 111 | --config modularqa_configs/hotpot_dev_config.json --reader hotpot 112 | ``` 113 | 114 | To evaluate on the held-out test set, change `dev` to `test` in the command above. 115 | 116 | To evaluate the DROP set, run: 117 | ``` 118 | python -u -m modularqa.inference.configurable_inference \ 119 | --input drop_subset/dev.json \ 120 | --output predictions_drop_dev.json \ 121 | --config modularqa_configs/drop_dev_config.json --reader drop 122 | ``` 123 | Similary replace `dev` with `test` to evaluate on the held-out test set. 124 | 125 | NOTE: These are slow inference steps but highly parallelizable. If you want, you can directly use our predictions available above. 126 | 127 | 5. Compute the metrics using the evaluation scripts released with the HotpotQA and DROP dataset. 128 | For example: 129 | 130 | * HotpotQA 131 | ``` 132 | python -m modularqa.evals.evaluate_hotpot_squad_format \ 133 | predictions_hotpot_dev.json hotpot_subset/dev.json 134 | ``` 135 | 136 | * DROP 137 | ``` 138 | python -m modularqa.evals.drop_eval \ 139 | --gold_path drop_subset/dev.json \ 140 | --prediction_path predictions_drop_dev.json \ 141 | ``` 142 | 143 | 144 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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