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Establishing whether language models can use contextual information in a human-plausible way is important to ensure their trustworthiness in real-world settings.
An evaluation of the human-interpretability of explanation
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, and Finale Doshi-Velez · 1902
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On information and sufficiency
Solomon Kullback and Richard A Leibler · 1951
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
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Overview of the IWSLT 2017 evaluation campaign
Mauro Cettolo, Marcello Federico, Luisa Bentivogli, Jan Niehues, Sebastian Stüker, Katsuhito Sudoh, Koichiro Yoshino, and Christian Federmann · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Neural machine translation with extended context
Jörg Tiedemann and Yves Scherrer · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Evaluating discourse phenomena in neural machine translation
Rachel Bawden, Rico Sennrich, Alexandra Birch, and Barry Haddow · 2018
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Has machine translation achieved human parity? a case for document-level evaluation
Samuel Läubli, Rico Sennrich, and Martin Volk · 2018
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OpenSubtitles2018: Statistical rescoring of sentence alignments in large, noisy parallel corpora
Pierre Lison, Jörg Tiedemann, and Milen Kouylekov · 2018
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A large-scale test set for the evaluation of context-aware pronoun translation in neural machine translation
Mathias Müller, Annette Rios, Elena Voita, and Rico Sennrich · 2018
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A call for clarity in reporting BLEU scores
Matt Post · 2018
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Attaining the unattainable? reassessing claims of human parity in neural machine translation
Antonio Toral, Sheila Castilho, Ke Hu, and Andy Way · 2018
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Context-aware neural machine translation learns anaphora resolution
Elena Voita, Pavel Serdyukov, Rico Sennrich, and Ivan Titov · 2018
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Attention is not Explanation
Sarthak Jain and Byron C. Wallace · 2019
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When and why is document-level context useful in neural machine translation?
Yunsu Kim, Duc Thanh Tran, and Hermann Ney · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen · 2019
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Evaluating gender bias in machine translation
Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer · 2019
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Context-aware monolingual repair for neural machine translation
Elena Voita, Rico Sennrich, and Ivan Titov · 2019
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2019
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Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema · 2020
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A diagnostic study of explainability techniques for text classification
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein · 2020
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The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?
Jasmijn Bastings and Katja Filippova · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 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
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FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Esin Durmus, He He, and Mona Diab · 2020
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Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
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Attention is not only a weight: Analyzing transformers with vector norms
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui · 2020
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Evaluating the factual consistency of abstractive text summarization
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher · 2020
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Document-level neural MT: A systematic comparison
Quality-aware decoding for neural machine translation
Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, and Andre Martins · 2022
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Towards opening the black box of neural machine translation: Source and target interpretations of the transformer
Javier Ferrando, Gerard I. Gállego, Belen Alastruey, Carlos Escolano, and Marta R. Costa-jussà · 2022
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Measuring the mixing of contextual information in the transformer
Javier Ferrando, Gerard I. Gállego, and Marta R. Costa-jussà · 2022
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Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space
Mor Geva, Avi Caciularu, Kevin Wang, and Yoav Goldberg · 2022
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The Flores-101 evaluation benchmark for low-resource and multilingual machine translation
Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, and Angela Fan · 2022
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António Lopes, M. Amin Farajian, Rachel Bawden, Michael Zhang, and André F. T. Martins · 2020
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald · 2020
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OPUS-MT – building open translation services for the world
Jörg Tiedemann and Santhosh Thottingal · 2020
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BLiMP: The benchmark of linguistic minimal pairs for English
Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, and Samuel R. Bowman · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush · 2020
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Word alignment by fine-tuning embeddings on parallel corpora
Zi-Yi Dou and Graham Neubig · 2021
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Measuring and increasing context usage in context-aware machine translation
Patrick Fernandes, Kayo Yin, Graham Neubig, and André F. T. Martins · 2021
Cited alongside, same era.
Post-hoc interpretability for neural nlp: A survey
Andreas Madsen, Siva Reddy, and Sarath Chandar · 2022
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A baseline revisited: pushing the limits of multi-segment models for context-aware translation
Suvodeep Majumder, Stanislas Lauly, Maria Nadejde, Marcello Federico, and Georgiana Dinu · 2022
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COMET-22: Unbabel-IST 2022 submission for the metrics shared task
Ricardo Rei, José G. C. de Souza, Duarte Alves, Chrysoula Zerva, Ana C Farinha, Taisiya Glushkova, Alon Lavie, Luisa Coheur, and André F. T. Martins · 2022
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Reducing hallucinations in neural machine translation with feature attribution
Joel Tang, M. Fomicheva, and Lucia Specia · 2022
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As little as possible, as much as necessary: Detecting over- and undertranslations with contrastive conditioning
Jannis Vamvas and Rico Sennrich · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc V. Le, and Denny Zhou · 2022
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Interpreting language models with contrastive explanations
Kayo Yin and Graham Neubig · 2022
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ferret: a framework for benchmarking explainers on transformers
Giuseppe Attanasio, Eliana Pastor, Chiara Di Bonaventura, and Debora Nozza · 2023
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Eliciting latent predictions from transformers with the tuned lens
Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt · 2023
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David Dale, Elena Voita, Janice Lam, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Loïc Barrault, and Marta R. Costa-jussà · 2023
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Are character-level translations worth the wait? comparing byt5 and mt5 for machine translation
Lukas Edman, Gabriele Sarti, Antonio Toral, Gertjan van Noord, and Arianna Bisazza · 2023
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When does translation require context? a data-driven, multilingual exploration
Patrick Fernandes, Kayo Yin, Emmy Liu, André Martins, and Graham Neubig · 2023
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Explaining how transformers use context to build predictions
Javier Ferrando, Gerard I. Gállego, Ioannis Tsiamas, and Marta R. Costa-jussà · 2023
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
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Challenges in Context-Aware neural machine translation
Linghao Jin, Jacqueline He, Jonathan May, and Xuezhe Ma · 2023
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Quantifying context mixing in transformers
Hosein Mohebbi, Willem Zuidema, Grzegorz Chrupała, and Afra Alishahi · 2023
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Inseq: An interpretability toolkit for sequence generation models
Gabriele Sarti, Nils Feldhus, Ludwig Sickert, Oskar van der Wal, Malvina Nissim, and Arianna Bisazza · 2023
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Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou · 2023
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Zephyr: Direct distillation of lm alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf · 2023
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