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In Neural Machine Translation (NMT), each token prediction is conditioned on the source sentence and the target prefix (what has been previously translated at a decoding step).
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D 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 Ziegler, Jeffrey Wu, Clemens Winter, Chris 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 · 1901
Earlier work this paper cites.
Adding interpretable attention to neural translation models improves word alignment
Thomas Zenkel, Joern Wuebker, and John DeNero. 2019 · 1901
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Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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AER: do we need to “improve” our alignments?
David Vilar, Maja Popovic, and Hermann Ney. 2006 · 2006
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015 · 2015
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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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 · 2017
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Hallucinations in neural machine translation
Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. 2018 · 2018
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An analysis of encoder representations in transformer-based machine translation
Alessandro Raganato and Jörg Tiedemann. 2018 · 2018
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Context-aware neural machine translation learns anaphora resolution
Elena Voita, Pavel Serdyukov, Rico Sennrich, and Ivan Titov. 2018 · 2018
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What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Saliency-driven word alignment interpretation for neural machine translation
Shuoyang Ding, Hainan Xu, and Philipp Koehn. 2019 · 2019
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Jointly learning to align and translate with transformer models
Sarthak Garg, Stephan Peitz, Udhyakumar Nallasamy, and Matthias Paulik. 2019 · 2019
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Attention is not Explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
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On the word alignment from neural machine translation
Xintong Li, Guanlin Li, Lemao Liu, Max Meng, and Shuming Shi. 2019 · 2019
Cited alongside, same era.
fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
Attention is not only a weight: Analyzing transformers with vector norms
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui. 2020 · 2020
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Domain robustness in neural machine translation
Mathias Müller, Annette Rios, and Rico Sennrich. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Later among the works it cites.
Beyond english-centric multilingual machine translation
Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Michael Auli, and Armand Joulin. 2021 · 2021
Later among the works it cites.
Attention weights in transformer NMT fail aligning words between sequences but largely explain model predictions
Javier Ferrando and Marta R. Costa-jussà. 2021 · 2021
Later among the works it cites.
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Cited alongside, same era.
Is attention interpretable?
Sofia Serrano and Noah A. Smith. 2019 · 2019
Cited alongside, same era.
The bottom-up evolution of representations in the transformer: A study with machine translation and language modeling objectives
Elena Voita, Rico Sennrich, and Ivan Titov. 2019 · 2019
Cited alongside, same era.
Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema. 2020 · 2020
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On identifiability in transformers
Gino Brunner, Yang Liu, Damian Pascual, Oliver Richter, Massimiliano Ciaramita, and Roger Wattenhofer. 2020 · 2020
Cited alongside, same era.
Accurate word alignment induction from neural machine translation
Yun Chen, Yang Liu, Guanhua Chen, Xin Jiang, and Qun Liu. 2020 · 2020
Cited alongside, same era.
Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers
Hila Chefer, Shir Gur, and Lior Wolf. 2021a
Cited in the paper.
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui. 2021 · 2021
Later among the works it cites.
Language modeling, lexical translation, reordering: The training process of NMT through the lens of classical SMT
Elena Voita, Rico Sennrich, and Ivan Titov. 2021b · 2021
Later among the works it cites.
Interpreting gender bias in neural machine translation: Multilingual architecture matters
Marta R. Costa-jussà, Carlos Escolano, Christine Basta, Javier Ferrando, Roser Batlle, and Ksenia Kharitonova. 2022 · 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 · 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 · 2022
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