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An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction
AbstractDecisions of complex models for language understanding can be explained by limiting the inputs they are provided to a relevant subsequence of the original text — a rationale. Models that …...
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation
AbstractNatural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for tasks such as Question Answering …...
Rational LAMOL: A Rationale-based Lifelong Learning Framework
AbstractLifelong learning (LL) aims to train a neural network on a stream of tasks while retaining knowledge from previous tasks. However, many prior attempts in NLP still suffer from the …...