22 citations · 67 across the 10 of their papers we have counts for
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Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark
Guanhua Zhang, Bing Bai, Jian Liang +3
Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts. Models utilizing these superficial cl…
Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting
Guanhua Zhang, Bing Bai, Junqi Zhang +3
With the recent proliferation of the use of text classifications, researchers have found that there are certain unintended biases in text classification datasets. For example, text…
Generating Followup Questions for Interpretable Multi-hop Question Answering
Christopher Malon, Bing Bai
We propose a framework for answering open domain multi-hop questions in which partial information is read and used to generate followup questions, to finally be answered by a pretr…
Mitigating Annotation Artifacts in Natural Language Inference Datasets to Improve Cross-dataset Generalization Ability
Guanhua Zhang, Bing Bai, Junqi Zhang +3
Natural language inference (NLI) aims at predicting the relationship between a given pair of premise and hypothesis. However, several works have found that there widely exists a bi…
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets
Guanhua Zhang, Bing Bai, Jian Liang +5
Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process. However,…