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Found 3 papers, 3 papers with code
Challenges in Context-Aware Neural Machine Translation
Context-aware neural machine translation involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, and has given rise to a number of recent techniques.
MABEL: Attenuating Gender Bias using Textual Entailment Data
To this end, we propose MABEL (a Method for Attenuating Gender Bias using Entailment Labels), an intermediate pre-training approach for mitigating gender bias in contextualized representations.
Can Rationalization Improve Robustness?
Our experiments reveal that the rationale models show the promise to improve robustness, while they struggle in certain scenarios--when the rationalizer is sensitive to positional bias or lexical choices of attack text.
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