timothyxxx / explanationlmpapers

3. ExplanationLMPapers(personnal usage)

   

   Paper collections of explanation-based(augmented) language model.

   Papers

   1. WT5?! Training Text-to-Text Models to Explain their Predictions. arxiv.

      Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, Karishma Malkan [pdf] 2020.04

   2. Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations. ACL

      Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini, Thomas Lukasiewicz, Phil Blunsom [pdf] 2020.05

   3. ExpBERT: Representation Engineering with Natural Language Explanations. ACL.

      Shikhar Murty, Pang Wei Koh, Percy Liang [pdf] 2020.05

   4. Staying True to Your Word: (How) Can Attention Become Explanation? ACL-repl4nlp.

      Martin Tutek, Jan Sˇnajder [pdf]

   5. FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation. EMNLP.

      Kushal Lakhotia, Bhargavi Paranjape, Asish Ghoshal, Wen-tau Yih, Yashar Mehdad, Srinivasan Iyer [pdf] 2020.12

   6. Training Verifiers to Solve Math Word Problems. arxiv.

      JKarl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman [pdf] 2021.10

   7. ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning. ACL

      Li Du, Xiao Ding, Kai Xiong, Ting Liu, and Bing Qin [pdf] 2021

   8. KACE: Generating Knowledge-Aware Contrastive Explanations for Natural Language Inference. ACL

      Qianglong Chen, Feng Ji, Xiangji Zeng, Feng-Lin Li, Ji Zhang, Haiqing Chen, Yin Zhang [pdf] 2021

   9. Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving. ACL

      Shih-hung Tsai, Chao-Chun Liang, Hsin-Min Wang, Keh-Yih Su [pdf] 2021

   10. Prompting Contrastive Explanations for Commonsense Reasoning Tasks. arxiv

       Bhargavi Paranjape, Julian Michael, Marjan Ghazvininejad, Luke Zettlemoyer†, Hannaneh Hajishirzi† [pdf] 2021.06

   11. The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? EMNLP-BlackboxNLP

       Jasmijn Bastings, Katja Filippova [pdf] 2021

   12. KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning. EMNLP

       Haonan Li, Yeyun Gong, Jian Jiao, Ruofei Zhang, Timothy Baldwin, Nan Duan [pdf] 2021

   13. Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension. EMNLP

       Naoya Inoue, Harsh Trivedi, Steven Sinha, Niranjan Balasubramanian, Kentaro Inui [pdf] 2021.09

   14. On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings. EMNLP

       Peter A. Jansen, Kelly Smith, Dan Moreno, Huitzilin Ortiz [pdf] 2021

   15. Exploiting Reasoning Chains for Multi-hop Science Question Answering. EMNLP

       Weiwen Xu, Yang Deng, Huihui Zhang, Deng Cai, Wai Lam [pdf] 2021

   16. Chain of Thought Prompting Elicits Reasoning in Large Language Models. arxiv

       Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, Denny Zhou [pdf] 2022.01

   17. Great Truths are Always Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models. ACL-ARR

       Anonymous [pdf: no website] 2022

   18. EIGEN: Event Influence GENeration using Pre-trained Language Models. arxiv

       Aman Madaan, Dheeraj Rajagopal, Yiming Yang, Abhilasha Ravichander, Eduard Hovy, Shrimai Prabhumoye [pdf] 2020.10

   Survey

   1. A Survey of the State of Explainable AI for Natural Language Processing. AACL-IJCNLP.

      Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, Prithviraj Sen [pdf] 2020.10

   2. Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing. NeurIPS.

      Sarah Wiegreffe, Ana Marasović [pdf] 2021.02

   Dataset

   1. Explain Yourself! Leveraging Language Models for Commonsense Reasoning. ACL.

      Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, Richard Socher [pdf] 2019.06

   2. Explanations for CommonsenseQA: New Dataset and Models. ACL.

      Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, Dinesh Garg [pdf] 2021.09

   3. WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference. ACL

      Peter A. Jansen , Elizabeth Wainwright , Steven Marmorstein , Clayton T. Morrison [pdf] 2018

   4. QASC: A Dataset for Question Answering via Sentence Composition. AAAI

      Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, Ashish Sabharwal [pdf] 2020.04

   5. PRover: Proof generation for interpretable reasoning over rules. EMNLP

      Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, Mohit Bansal [pdf] 2020.11

   6. multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning. NAACL

      Swarnadeep Saha, Prateek Yadav, Mohit Bansal [pdf] 2021.06

   Symbolic (Special Section)

   

   1. DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs. arxiv.

      Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner [pdf] 2019.03

   2. Break It Down: A Question Understanding Benchmark. TACL.

      Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, Jonathan Berant [pdf] 2019.03

   3. Break It Down: A Question Understanding Benchmark. TACL.

      Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, Jonathan Berant [pdf] 2019.03