Built on

Attention is not explanation
Original
Sarthak Jain and Byron C Wallace. 2019 · 1902
Earlier work this paper cites.
Is attention interpretable?
Original
Sofia Serrano and Noah A Smith. 2019 · 1906
Earlier work this paper cites.
Attention is not not explanation
Original
Sarah Wiegreffe and Yuval Pinter. 2019 · 1908
Earlier work this paper cites.
The bottom-up evolution of representations in the transformer: A study with machine translation and language modeling objectives
Original
Elena Voita, Rico Sennrich, and Ivan Titov. 2019 · 1909
Earlier work this paper cites.
Rethinking cooperative rationalization: Introspective extraction and complement control
Original
Mo Yu, Shiyu Chang, Yang Zhang, and Tommi S Jaakkola. 2019 · 1910
Earlier work this paper cites.
Unsupervised cross-lingual representation learning at scale
Original
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1911
Earlier work this paper cites.
Eraser: A benchmark to evaluate rationalized nlp models
Original
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C Wallace. 2019 · 1911
Earlier work this paper cites.
Restricting the flow: Information bottlenecks for attribution
Original
Karl Schulz, Leon Sixt, Federico Tombari, and Tim Landgraf. 2020 · 2001
Earlier work this paper cites.
Confidence estimation for machine translation
John Blatz, Erin Fitzgerald, George Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchis, and Nicola Ueffing. 2004 · 2004
Earlier work this paper cites.
How do decisions emerge across layers in neural models? interpretation with differentiable masking
Original
Nicola De Cao, Michael Schlichtkrull, Wilker Aziz, and Ivan Titov. 2020 · 2004
Earlier work this paper cites.
Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Original
Alon Jacovi and Yoav Goldberg. 2020 · 2004
Earlier work this paper cites.
Unsupervised quality estimation for neural machine translation
Original
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, and Lucia Specia. 2020b · 2005
Earlier work this paper cites.

Similar

Learning to faithfully rationalize by construction
Original
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C Wallace. 2020 · 2005
Cited alongside, same era.
A study of translation edit rate with targeted human annotation
Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006 · 2006
Cited alongside, same era.
Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
Cited alongside, same era.
A diagnostic study of explainability techniques for text classification
Original
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020 · 2009
Cited alongside, same era.
Estimating the sentence-level quality of machine translation systems
Lucia Specia, Nicola Cancedda, Marc Dymetman, Marco Turchi, and Nello Cristianini. 2009 · 2009
Cited alongside, same era.

Then

" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Later among the works it cites.
Predictor-estimator using multilevel task learning with stack propagation for neural quality estimation
Hyun Kim, Jong-Hyeok Lee, and Seung-Hoon Na. 2017 · 2017
Later among the works it cites.
Confidence through attention
Original
Matīss Rikters and Mark Fishel. 2017 · 2017
Later among the works it cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Original
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Reading the bibliography…

2021

Translation Error Detection as Rationale Extraction

Fomicheva, Marina, Specia, Lucia, Aletras, Nikolaos

Understand

Recent Quality Estimation (QE) models based on multilingual pre-trained representations have achieved very competitive results when predicting the overall quality of translated sentences.

Predicting translation errors, i.e.
detecting specifically which words are incorrect, is a more challenging task, especially with limited amounts of training data.
We hypothesize that, not unlike humans, successful QE models rely on translation errors to predict overall sentence quality.

Built on

Attention is not explanation
Original
Sarthak Jain and Byron C Wallace. 2019 · 1902
Earlier work this paper cites.
Is attention interpretable?
Original
Sofia Serrano and Noah A Smith. 2019 · 1906
Earlier work this paper cites.
Attention is not not explanation
Original
Sarah Wiegreffe and Yuval Pinter. 2019 · 1908
Earlier work this paper cites.
The bottom-up evolution of representations in the transformer: A study with machine translation and language modeling objectives
Original
Elena Voita, Rico Sennrich, and Ivan Titov. 2019 · 1909
Earlier work this paper cites.
Rethinking cooperative rationalization: Introspective extraction and complement control
Original
Mo Yu, Shiyu Chang, Yang Zhang, and Tommi S Jaakkola. 2019 · 1910
Earlier work this paper cites.
Unsupervised cross-lingual representation learning at scale
Original
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1911
Earlier work this paper cites.
Eraser: A benchmark to evaluate rationalized nlp models
Original
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C Wallace. 2019 · 1911
Earlier work this paper cites.
Restricting the flow: Information bottlenecks for attribution
Original
Karl Schulz, Leon Sixt, Federico Tombari, and Tim Landgraf. 2020 · 2001
Earlier work this paper cites.
Confidence estimation for machine translation
John Blatz, Erin Fitzgerald, George Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchis, and Nicola Ueffing. 2004 · 2004
Earlier work this paper cites.
How do decisions emerge across layers in neural models? interpretation with differentiable masking
Original
Nicola De Cao, Michael Schlichtkrull, Wilker Aziz, and Ivan Titov. 2020 · 2004
Earlier work this paper cites.
Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Original
Alon Jacovi and Yoav Goldberg. 2020 · 2004
Earlier work this paper cites.
Unsupervised quality estimation for neural machine translation
Original
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, and Lucia Specia. 2020b · 2005
Earlier work this paper cites.

Similar

Learning to faithfully rationalize by construction
Original
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C Wallace. 2020 · 2005
Cited alongside, same era.
A study of translation edit rate with targeted human annotation
Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006 · 2006
Cited alongside, same era.
Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
Cited alongside, same era.
A diagnostic study of explainability techniques for text classification
Original
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020 · 2009
Cited alongside, same era.
Estimating the sentence-level quality of machine translation systems
Lucia Specia, Nicola Cancedda, Marc Dymetman, Marco Turchi, and Nello Cristianini. 2009 · 2009
Cited alongside, same era.

Then

" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Later among the works it cites.
Predictor-estimator using multilevel task learning with stack propagation for neural quality estimation
Hyun Kim, Jong-Hyeok Lee, and Seung-Hoon Na. 2017 · 2017
Later among the works it cites.
Confidence through attention
Original
Matīss Rikters and Mark Fishel. 2017 · 2017
Later among the works it cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Original
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Mlqe-pe: A multilingual quality estimation and post-editing dataset

Original

Marina Fomicheva, Shuo Sun, Erick Fonseca, Frédéric Blain, Vishrav Chaudhary, Francisco Guzmán, Nina Lopatina, Lucia Specia, and André FT Martins. 2020a · 2010

Cited alongside, same era.

Transquest at wmt2020: Sentence-level direct assessment

Original

Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov. 2020a · 2010

Cited alongside, same era.

Transquest: Translation quality estimation with cross-lingual transformers

Original

Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov. 2020b · 2011

Cited alongside, same era.

Deep inside convolutional networks: Visualising image classification models and saliency maps

Original

Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013