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2021-08
Post-hoc Interpretability for Neural NLP: A Survey
Minimax and Neyman-Pearson Meta-Learning for Outlier Languages
StereoSet: Measuring stereotypical bias in pretrained language models
2021-06
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering.
2021-05
Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle.
Understanding by Understanding Not: Modeling Negation in Language Models.
2021-04
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval.
2020-11
Universal Dependencies 2.7
MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
EMNLP 2020
(2020-11-01)
virtual.2020.emnlp.orgPDF[LATEST on arXiv preprint arXiv:2012.13978 (2020-12-27)]Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback
2020-10
Explicitly Modeling Syntax in Language Model improves Generalization.
2020-05
Universal Dependencies 2.6
Words aren’t enough, their order matters: on the robustness of grounding visual referring expressions
2020-01
You could have said that instead: Improving Chatbots with Natural Language Feedback.
2019-11
Universal Dependencies 2.5
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