Neural network models have achieved state-of-the-art performances in a wide range of
natural language processing (NLP) tasks. However, a long-standing criticism against neural …

Rationale extraction can be considered as a straightforward method of improving the model
explainability, where rationales are a subsequence of the original inputs, and can be …

Rationalization aims to strengthen the interpretability of NLP models by extracting a subset
of human-intelligible pieces of their inputting texts. Conventional works generally employ a …

Most existing rationalization approaches are susceptible to degeneration accumulation due
to a lack of effective control over the learning direction of the model during training. To …

A self-explaining rationalization model is generally constructed by a cooperative game
where a generator selects the most human-intelligible pieces from the input text as …

Rationalization empowers deep learning models with self-explaining capabilities through a
cooperative game, where a generator selects a semantically consistent subset of the input …

Rationalization aims to strengthen the interpretability of NLP models by extracting a subset
of human-intelligible pieces of their inputting texts. Conventional works generally employ the …

Rationalization is to employ a generator and a predictor to construct a self-explaining NLP
model in which the generator selects a subset of human-intelligible pieces of the input text to …

Unsupervised rationale extraction aims to extract text snippets to support model predictions
without explicit rationale annotation. Researchers have made many efforts to solve this task …

Selective rationalization can be regarded as a straightforward self-explaining approach for
enhancing model explainability in natural language processing tasks. It aims to provide …