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Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models are surprisingly insensitive to the order of words.
Assessing bert’s syntactic abilities
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The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
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Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)
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The third PASCAL recognizing textual entailment challenge
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A normalized levenshtein distance metric
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A unified architecture for natural language processing: Deep neural networks with multitask learning
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The fifth PASCAL recognizing textual entailment challenge
Luisa Bentivogli, Ido Dagan, Hoa Trang Dang, Danilo Giampiccolo, and Bernardo Magnini. 2009 · 2009
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The Winograd schema challenge
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Thang M. Pham, Trung Bui, Long Mai, and Anh M Nguyen. 2021 · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
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Language learners privilege structured meaning over surface frequency
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Self-adaptive hierarchical sentence model
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Improving zero-shot translation with language-independent constraints
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Do neural dialog systems use the conversation history effectively? an empirical study
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Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
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Neural network acceptability judgments
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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John Hale. 2017 · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Hotflip: White-box adversarial examples for text classification
J. Ebrahimi, Anyi Rao, Daniel Lowd, and D. Dou. 2018 · 2018
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Black-box generation of adversarial text sequences to evade deep learning classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi. 2018 · 2018
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Colorless green recurrent networks dream hierarchically
Kristina Gulordava, Piotr Bojanowski, Edouard Grave, Tal Linzen, and Marco Baroni. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford and Karthik Narasimhan. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018
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What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models
Allyson Ettinger. 2020 · 2020
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Dependency locality as an explanatory principle for word order
Richard Futrell, Roger P Levy, and Edward Gibson. 2020 · 2020
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Is supervised syntactic parsing beneficial for language understanding? an empirical investigation
Goran Glavaš and Ivan Vulić. 2020 · 2020
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Wentao Ma, Yiming Cui, Chenglei Si, Ting Liu, Shijin Wang, and Guoping Hu. 2020 · 2020
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Composition is the Core Driver of the Language-selective Network
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BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
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Learning which features matter: RoBERTa acquires a preference for linguistic generalizations (eventually)
Alex Warstadt, Yian Zhang, Xiaocheng Li, Haokun Liu, and Samuel R. Bowman. 2020 · 2020
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Canine: Pre-training an efficient tokenization-free encoder for language representation
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Sometimes we want translationese
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Charformer: Fast character transformers via gradient-based subword tokenization
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