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nlp:question_answering [2022/09/17 01:35] – [Explanation And Implicit Reasoning Papers] jmflanignlp:question_answering [2025/05/13 19:46] (current) – [Overviews] jmflanig
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   * **[[https://arxiv.org/ftp/arxiv/papers/2001/2001.01582.pdf|Baradaran et al 2020 - A Survey on Machine Reading Comprehension Systems]]**   * **[[https://arxiv.org/ftp/arxiv/papers/2001/2001.01582.pdf|Baradaran et al 2020 - A Survey on Machine Reading Comprehension Systems]]**
   * [[https://arxiv.org/pdf/2010.00389.pdf|Thayaparan et al 2020 - A Survey on Explainability in Machine Reading Comprehension]]   * [[https://arxiv.org/pdf/2010.00389.pdf|Thayaparan et al 2020 - A Survey on Explainability in Machine Reading Comprehension]]
 +  * **[[https://arxiv.org/pdf/2107.12708.pdf|Rogers et al 2022 - QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension]]** [[https://dl.acm.org/doi/pdf/10.1145/3560260|ACM version (better)]]
 ===== Demos ===== ===== Demos =====
   * [[https://demo.allennlp.org/reading-comprehension/transformer-qa|AllenNLP - RoBERTa QA Model Online Demo]]   * [[https://demo.allennlp.org/reading-comprehension/transformer-qa|AllenNLP - RoBERTa QA Model Online Demo]]
  
 ===== Key Papers ===== ===== Key Papers =====
 +  * Early papers
 +    * [[https://aclanthology.org/W00-0603.pdf|Riloff & Thelen 2000 - A Rule-based Question Answering System for Reading Comprehension Tests]] (Cited by the SQuAD 1.0 paper)
   * [[https://arxiv.org/pdf/1606.02858v2.pdf|Chen et al 2016 - A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task]]   * [[https://arxiv.org/pdf/1606.02858v2.pdf|Chen et al 2016 - A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task]]
 +  * [[https://arxiv.org/pdf/1606.05250.pdf|Rajpurkar et al 2016 - SQuAD: 100,000+ Questions for Machine Comprehension of Text]]
   * BiDAF model   * BiDAF model
 +  * [[https://arxiv.org/pdf/1806.03822.pdf|Rajpurkar et al 2018 - Know What You Don't Know: Unanswerable Questions for SQuAD]] (SQuAD 2.0 paper)
   * [[https://aclanthology.org/2020.emnlp-main.550.pdf|Karpukhin et al 2020 - Dense Passage Retrieval for Open-Domain Question Answering]]   * [[https://aclanthology.org/2020.emnlp-main.550.pdf|Karpukhin et al 2020 - Dense Passage Retrieval for Open-Domain Question Answering]]
 +  * [[https://arxiv.org/pdf/2404.06283|Basmov et al 2024 - LLMs’ Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements]]
 +
 ====== Topics ====== ====== Topics ======
  
 ===== General QA Papers ===== ===== General QA Papers =====
   * [[https://arxiv.org/pdf/1601.01705.pdf|Andreas et al 2016 - Learning to Compose Neural Networks for Question Answering]]   * [[https://arxiv.org/pdf/1601.01705.pdf|Andreas et al 2016 - Learning to Compose Neural Networks for Question Answering]]
 +  * [[https://arxiv.org/pdf/2404.06283|Basmov et al 2024 - LLMs’ Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements]]
  
 ===== Explanation And Implicit Reasoning Papers ===== ===== Explanation And Implicit Reasoning Papers =====
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   * [[https://arxiv.org/pdf/2101.02235.pdf|Geva et al 2021 - Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies]]   * [[https://arxiv.org/pdf/2101.02235.pdf|Geva et al 2021 - Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies]]
   * [[https://arxiv.org/pdf/2104.08661.pdf|Dalvi et al 2021 - Explaining Answers with Entailment Trees]]   * [[https://arxiv.org/pdf/2104.08661.pdf|Dalvi et al 2021 - Explaining Answers with Entailment Trees]]
 +
 +===== QA with Attribution =====
 +  * [[https://arxiv.org/pdf/2212.08037|Bohnet et al 2022 - Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models]]
 +
 ===== Robust Question Answering ===== ===== Robust Question Answering =====
-  * [[https://arxiv.org/pdf/2004.14648.pdf|Robust Question Answering Through Sub-part Alignment]]+  * [[https://arxiv.org/pdf/2004.14648.pdf|Chen & Durrett 2020 - Robust Question Answering Through Sub-part Alignment]]
  
 ===== Open-Domain Question Answering ===== ===== Open-Domain Question Answering =====
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 ===== Yes/No Questions ===== ===== Yes/No Questions =====
   * [[https://arxiv.org/pdf/1905.10044.pdf|Clark et al 2019 - BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions]]   * [[https://arxiv.org/pdf/1905.10044.pdf|Clark et al 2019 - BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions]]
 +  * [[https://aclanthology.org/2022.naacl-main.79.pdf|Sulem et al 2021 - Yes, No or IDK: The Challenge of Unanswerable Yes/No Questions]]
  
 ===== Long-Form QA ===== ===== Long-Form QA =====
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 ===== Domain Adaptation ===== ===== Domain Adaptation =====
-See the related work in [[https://arxiv.org/pdf/2203.08926.pdf|Yue 2022]], and also [[https://scholar.google.com/citations?user=VKKAfwMAAAAJ&hl=en|Arafat Sultan's publications on QA]].+See the related work in [[https://arxiv.org/pdf/2203.08926.pdf|Yue 2022]] and [[https://arxiv.org/pdf/2210.10861.pdf|Yue 2022]], and also [[https://scholar.google.com/citations?user=VKKAfwMAAAAJ&hl=en|Arafat Sultan's publications on QA]].
  
 === Synthetic Question Generation === === Synthetic Question Generation ===
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   * [[https://aclanthology.org/D17-1090.pdf|Duan et al 2017 - Question Generation for Question Answering]]   * [[https://aclanthology.org/D17-1090.pdf|Duan et al 2017 - Question Generation for Question Answering]]
-  * [[https://dl.acm.org/doi/abs/10.1145/3404835.3463085|2021 - Synthetic Target Domain Supervision for Open Retrieval QA]]+  * [[https://arxiv.org/pdf/1706.02027.pdf|Tang et al 2017 - Question Answering and Question Generation as Dual Tasks]]
   * [[https://aclanthology.org/2020.emnlp-main.439.pdf|Shakeri et al 2020 - End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems]] Generates QA pairs in the target domain using encoder-decoder large pre-trained model fine-tuned to the dataset.   * [[https://aclanthology.org/2020.emnlp-main.439.pdf|Shakeri et al 2020 - End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems]] Generates QA pairs in the target domain using encoder-decoder large pre-trained model fine-tuned to the dataset.
   * [[https://arxiv.org/pdf/2012.01414.pdf|Reddy et al 2020 - End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training]]   * [[https://arxiv.org/pdf/2012.01414.pdf|Reddy et al 2020 - End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training]]
 +  * [[https://arxiv.org/pdf/2010.06028.pdf|Shakeri et al 2020 - End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems]]
   * **[[https://arxiv.org/pdf/2010.12776.pdf|Chen et al 2020 - Improved Synthetic Training for Reading Comprehension]]**   * **[[https://arxiv.org/pdf/2010.12776.pdf|Chen et al 2020 - Improved Synthetic Training for Reading Comprehension]]**
   * [[https://arxiv.org/pdf/2010.16021.pdf|2020 - CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering]]   * [[https://arxiv.org/pdf/2010.16021.pdf|2020 - CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering]]
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   * [[https://arxiv.org/pdf/2203.08926.pdf|Yue et al 2022 - Synthetic Question Value Estimation   * [[https://arxiv.org/pdf/2203.08926.pdf|Yue et al 2022 - Synthetic Question Value Estimation
 for Domain Adaptation of Question Answering]] for Domain Adaptation of Question Answering]]
 +  * [[https://arxiv.org/pdf/2204.09248.pdf|Reddy et al 2022 - Synthetic Target Domain Supervision for Open Retrieval QA]]
 +  * [[https://arxiv.org/pdf/2210.10861.pdf|Yue et al 2022 - QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation]]
  
 ===== Cross-Lingual ===== ===== Cross-Lingual =====
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 ====== Datasets ====== ====== Datasets ======
-See also [[http://nlpprogress.com/english/question_answering.html|NLP Progress - Question Answering]] and [[https://docs.google.com/spreadsheets/d/1gWDy7-rfT0efhmFF42fR9cPpfqZDKl07q1JxrRqGEVI/edit#gid=0|Geetanjali's QA Datasets Spreadsheet]]+See [[https://arxiv.org/pdf/2107.12708.pdf|Rogers et al 2022 - QA Dataset Explosion]]. See also [[http://nlpprogress.com/english/question_answering.html|NLP Progress - Question Answering]] and [[https://docs.google.com/spreadsheets/d/1gWDy7-rfT0efhmFF42fR9cPpfqZDKl07q1JxrRqGEVI/edit#gid=0|Geetanjali's QA Datasets Spreadsheet]]
   * **CNN/Daily Mail Reading Comprehension**   * **CNN/Daily Mail Reading Comprehension**
     * Large-scale cloze-style QA dataset constructed from news articles and their summaries     * Large-scale cloze-style QA dataset constructed from news articles and their summaries
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   * **WinoGrande** (Sakaguchi et al., 2020): A large scale version of WSC that exhibits less bias thanks to adversarial filtering and use of placeholders instead of pronouns. As opposed to WSC that was curated by experts, WinoGrande was crowdsourced with a carefully designed approach that produces diverse examples which are trivial for humans. (Summary from [[https://arxiv.org/pdf/2004.05483.pdf|Shwartz 2020]])   * **WinoGrande** (Sakaguchi et al., 2020): A large scale version of WSC that exhibits less bias thanks to adversarial filtering and use of placeholders instead of pronouns. As opposed to WSC that was curated by experts, WinoGrande was crowdsourced with a carefully designed approach that produces diverse examples which are trivial for humans. (Summary from [[https://arxiv.org/pdf/2004.05483.pdf|Shwartz 2020]])
   * **HybridQA**: [[https://arxiv.org/pdf/2004.07347.pdf|Chen et al 2020 - HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data]]   * **HybridQA**: [[https://arxiv.org/pdf/2004.07347.pdf|Chen et al 2020 - HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data]]
 +  * **UnifiedQA**: [[https://arxiv.org/pdf/2005.00700.pdf|Khashabi et al 2020 - UnifiedQA: Crossing Format Boundaries With a Single QA System]]
   * Open Domain   * Open Domain
     *  **Natural Questions**     *  **Natural Questions**
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   * Product Domain (Product-related Question Answering - PQA)   * Product Domain (Product-related Question Answering - PQA)
     * **Amazon-PQA** and **AmazonPQSim**: [[https://aclanthology.org/2021.naacl-main.23.pdf|Rozen et al 2021 - Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products]] Worked with Yes/No questions only.  Datasets available [[https://registry.opendata.aws/|here]] (search for [[https://registry.opendata.aws/amazon-pqa/|Amazon-PQA]] or AmazonPQSim). Dataset incluses free-form, not extractive answers and yes/no questions.     * **Amazon-PQA** and **AmazonPQSim**: [[https://aclanthology.org/2021.naacl-main.23.pdf|Rozen et al 2021 - Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products]] Worked with Yes/No questions only.  Datasets available [[https://registry.opendata.aws/|here]] (search for [[https://registry.opendata.aws/amazon-pqa/|Amazon-PQA]] or AmazonPQSim). Dataset incluses free-form, not extractive answers and yes/no questions.
 +  * Research Paper Domain
 +    * **Qasper**: [[https://arxiv.org/pdf/2105.03011.pdf|Dasigi et al 2021 - A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers]] This dataset is really hard. Contains everything: abstractive, multispan extractive, yes / no, unanswerable questions, all from a very long context.
  
 ====== Resources ====== ====== Resources ======
nlp/question_answering.1663378500.txt.gz · Last modified: 2023/06/15 07:36 (external edit)
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