Fetching the paper…
Reading the bibliography…
Natural language free-text explanation generation is an efficient approach to train explainable language processing models for commonsense-knowledge-requiring tasks.
An information bottleneck approach for controlling conciseness in rationale extraction
Bhargavi Paranjape, Mandar Joshi, John Thickstun, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020 · 1952
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Explaining question answering models through text generation
Veronica Latcinnik and Jonathan Berant. 2020 · 2004
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Wt5?! training text-to-text models to explain their predictions
Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, and Karishma Malkan. 2020 · 2004
Earlier work this paper cites.
METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
Earlier work this paper cites.
Unconscious determinants of free decisions in the human brain
Chun Siong Soon, Marcel Brass, Hans-Jochen Heinze, and John-Dylan Haynes. 2008 · 2008
Earlier work this paper cites.
Jonathan Pilault, Amine Elhattami, and Christopher Pal. 2020 · 2009
Earlier work this paper cites.
Measuring association between labels and free-text rationales
Sarah Wiegreffe, Ana Marasovic, and Noah A Smith. 2020 · 2010
Earlier work this paper cites.
Fusing context into knowledge graph for commonsense reasoning
Yichong Xu, Chenguang Zhu, Ruochen Xu, Yang Liu, Michael Zeng, and Xuedong Huang. 2020 · 2012
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Categorical reparameterization with Gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
Cited alongside, same era.
Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Cited alongside, same era.
e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
Cited alongside, same era.
Universal sentence encoder for English
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, and Ray Kurzweil. 2018 · 2018
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
Cited alongside, same era.
A call for clarity in reporting BLEU scores
Predicting annotation difficulty to improve task routing and model performance for biomedical information extraction
Yinfei Yang, Oshin Agarwal, Chris Tar, Byron C. Wallace, and Ani Nenkova. 2019a · 2019
Later among the works it cites.
Rethinking cooperative rationalization: Introspective extraction and complement control
Mo Yu, Shiyu Chang, Yang Zhang, and Tommi Jaakkola. 2019 · 2019
Later among the works it cites.
Make up your mind! adversarial generation of inconsistent natural language explanations
Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini, Thomas Lukasiewicz, and Phil Blunsom. 2020 · 2020
Later among the works it cites.
Quantifying exposure bias for open-ended language generation
Tianxing He, Jingzhao Zhang, Zhiming Zhou, and James Glass. 2020 · 2020
Later among the works it cites.
Learning to faithfully rationalize by construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C. Wallace. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Matt Post. 2018 · 2018
Cited alongside, same era.
Interpretable neural predictions with differentiable binary variables
Jasmijn Bastings, Wilker Aziz, and Ivan Titov. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Cited alongside, same era.
Generalization in generation: A closer look at exposure bias
Florian Schmidt. 2019 · 2019
Cited alongside, same era.
CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
Cited alongside, same era.
XLNet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019b
Cited in the paper.
NILE : Natural language inference with faithful natural language explanations
Sawan Kumar and Partha Talukdar. 2020 · 2020
Later among the works it cites.
Weakly- and semi-supervised evidence extraction
Danish Pruthi, Bhuwan Dhingra, Graham Neubig, and Zachary C. Lipton. 2020 · 2020
Later among the works it cites.
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.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2020 · 2020
Later among the works it cites.
Lirex: Augmenting language inference with relevant explanation
Xinyan Zhao and VG Vydiswaran. 2021 · 2020
Later among the works it cites.
You can do better! if you elaborate the reason when making prediction
Dongfang Li, Jingcong Tao, Qingcai Chen, and Baotian Hu. 2021 · 2021
Closest in time.