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Found 8 papers, 3 papers with code
On the Interaction of Belief Bias and Explanations
A myriad of explainability methods have been proposed in recent years, but there is little consensus on how to evaluate them.
Does injecting linguistic structure into language models lead to better alignment with brain recordings?
We evaluate across two fMRI datasets whether language models align better with brain recordings, if their attention is biased by annotations from syntactic or semantic formalisms.
Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA
While research on explaining predictions of open-domain QA systems (ODQA) to users is gaining momentum, most works have failed to evaluate the extent to which explanations improve user trust.
Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias
The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and WinoGender highlight model preferences that are "hallucinatory", e. g., disambiguating gender-ambiguous occurrences of 'doctor' as male doctors.
Retrieval-based Goal-Oriented Dialogue Generation
Most research on dialogue has focused either on dialogue generation for openended chit chat or on state tracking for goal-directed dialogue.
Domain Transfer in Dialogue Systems without Turn-Level Supervision
Task oriented dialogue systems rely heavily on specialized dialogue state tracking (DST) modules for dynamically predicting user intent throughout the conversation.
Rewarding Coreference Resolvers for Being Consistent with World Knowledge
Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples.
Naive Regularizers for Low-Resource Neural Machine Translation
Neural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios.
