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While recently developed NLP explainability methods let us open the black box in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool offering a conversational interface.
Combining feature and instance attribution to detect artifacts
Pouya Pezeshkpour, Sarthak Jain, Sameer Singh, and Byron Wallace. 2022 · 1946
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NLTK: The Natural Language Toolkit
Steven Bird. 2006 · 2006
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Questionnaires for eliciting evaluation data from users of interactive question answering systems
Diane Kelly, Paul B. Kantor, Emile L. Morse, Jean Scholtz, and Ying Sun. 2009 · 2009
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Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo. 2016 · 2016
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
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DailyDialog: A manually labelled multi-turn dialogue dataset
Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017 · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
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e-SNLI: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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HotFlip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018 · 2018
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Comparing automatic and human evaluation of local explanations for text classification
Dong Nguyen. 2018 · 2018
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019 · 2019
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Gamut: A design probe to understand how data scientists understand machine learning models
Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, and Steven M. Drucker. 2019 · 2019
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
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Can you explain that? lucid explanations help human-ai collaborative image retrieval
Arijit Ray, Yi Yao, Rakesh Kumar, Ajay Divakaran, and Giedrius Burachas. 2019 · 2019
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Generating natural language adversarial examples through probability weighted word saliency
Shuhuai Ren, Yihe Deng, Kun He, and Wanxiang Che. 2019 · 2019
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 2019
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Do human rationales improve machine explanations?
Julia Strout, Ye Zhang, and Raymond Mooney. 2019 · 2019
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The challenge of crafting intelligible intelligence
Daniel S. Weld and Gagan Bansal. 2019 · 2019
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Predicting the type and target of offensive posts in social media
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019 · 2019
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A diagnostic study of explainability techniques for text classification
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020 · 2020
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ERASER: A benchmark to evaluate rationalized NLP models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. 2020 · 2020
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Evaluating explainable AI: Which algorithmic explanations help users predict model behavior?
Peter Hase and Mohit Bansal. 2020 · 2020
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What would you ask the machine learning model? identification of user needs for model explanations based on human-model conversations
Michał Kuźba and Przemysław Biecek. 2020 · 2020
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AdapterHub: A framework for adapting transformers
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych. 2020 · 2020
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F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering
Hendrik Schuff, Heike Adel, and Ngoc Thang Vu. 2020 · 2020
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The language interpretability tool: Extensible, interactive visualizations and analysis for NLP models
Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, and Ann Yuan. 2020 · 2020
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Explainable ai through rule-based interactive conversation
Christian Werner. 2020 · 2020
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021 · 2021
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Few-shot self-rationalization with natural language prompts
Ana Marasovic, Iz Beltagy, Doug Downey, and Matthew Peters. 2022 · 2022
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Shikib Mehri, Jinho Choi, L. F. D’Haro, Jan Deriu, Maxine Eskénazi, Milica Gasic, Kallirroi Georgila, Dilek Z. Hakkani-Tür, Zekang Li, Verena Rieser, Samira Shaikh, David R. Traum, Yi-Ting Yeh, Zhou Yu, Yizhe Zhang, and Chen Zhang. 2022 · 2022
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SEAL: Interactive tool for systematic error analysis and labeling
Nazneen Rajani, Weixin Liang, Lingjiao Chen, Margaret Mitchell, and James Zou. 2022 · 2022
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Explaining classes through stable word attributions
Samuel Rönnqvist, Aki-Juhani Kyröläinen, Amanda Myntti, Filip Ginter, and Veronika Laippala. 2022 · 2022
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Tailor: Generating and perturbing text with semantic controls
Alexis Ross, Tongshuang Wu, Hao Peng, Matthew Peters, and Matt Gardner. 2022 · 2022
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Robustness gym: Unifying the NLP evaluation landscape
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On the interaction of belief bias and explanations
Ana Valeria González, Anna Rogers, and Anders Søgaard. 2021 · 2021
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Hatexplain: A benchmark dataset for explainable hate speech detection
Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee. 2021 · 2021
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Textual eXplanations for intuitive machine learning
Vittorio Torri. 2021 · 2021
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Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel Weld. 2021 · 2021
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OpenAttack: An open-source textual adversarial attack toolkit
Guoyang Zeng, Fanchao Qi, Qianrui Zhou, Tingji Zhang, Bairu Hou, Yuan Zang, Zhiyuan Liu, and Maosong Sun. 2021 · 2021
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Explain, edit, and understand: Rethinking user study design for evaluating model explanations
Siddhant Arora, Danish Pruthi, Norman Sadeh, William W. Cohen, Zachary C. Lipton, and Graham Neubig. 2022 · 2022
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Human interpretation of saliency-based explanation over text
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Evaluate & evaluation on the hub: Better best practices for data and model measurement
Leandro von Werra, Lewis Tunstall, Abhishek Thakur, Alexandra Sasha Luccioni, Tristan Thrush, Aleksandra Piktus, Felix Marty, Nazneen Rajani, Victor Mustar, Helen Ngo, Omar Sanseviero, Mario Sasko, Albert Villanova, Quentin Lhoest, Julien Chaumond, Margaret Mitchell, Alexander M. Rush, Thomas Wolf, and Douwe Kiela. 2022 · 2022
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Reframing human-AI collaboration for generating free-text explanations
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, and Yejin Choi. 2022 · 2022
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DataLab: A platform for data analysis and intervention
Yang Xiao, Jinlan Fu, Weizhe Yuan, Vijay Viswanathan, Zhoumianze Liu, Yixin Liu, Graham Neubig, and Pengfei Liu. 2022 · 2022
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On selective, mutable and dialogic XAI: A review of what users say about different types of interactive explanations
Astrid Bertrand, Tiphaine Viard, Rafik Belloum, James R. Eagan, and Winston Maxwell. 2023 · 2023
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Diagnosing ai explanation methods with folk concepts of behavior
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XMD: An end-to-end framework for interactive explanation-based debugging of NLP models
Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan, Ziyi Liu, Kiran Narahari, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, and Xiang Ren. 2023 · 2023
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Chameleon: Plug-and-play compositional reasoning with large language models
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Understanding user satisfaction with task-oriented dialogue systems
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