A Study of the Plausibility of Attention between RNN Encoders in Natural Language Inference

Attention maps in neural models for NLP are appealing to explain the decision made by a model, hopefully emphasizing words that justify the decision. While many empirical studies hint that attention maps can provide such justification from the analysis of sound examples, only a few assess the plausibility of explanations based on attention maps, i.e., the usefulness of attention maps for humans to understand the decision. These studies furthermore focus on text classification. In this paper, we report on a preliminary assessment of attention maps in a sentence comparison task, namely natural language inference. We compare the cross-attention weights between two RNN encoders with human-based and heuristic-based annotations on the eSNLI corpus. We show that the heuristic reasonably correlates with human annotations and can thus facilitate evaluation of plausible explanations in sentence comparison tasks. Raw attention weights however remain only loosely related to a plausible explanation.

Utilisation de la linguistique en reconnaissance de la parole : un état de l'art

To transcribe speech, automatic speech recognition systems use statistical methods, particularly hidden Markov model and N-gram models. Although these techniques perform well and lead to efficient systems, they approach their maximum possibilities. It seems thus necessary, in order to outperform current results, to use additional information, especially bound to language. However, introducing such knowledge must be realized taking into account specificities of spoken language (hesitations for example) and being robust to possible misrecognized words. This document presents a state of the art of these researches, evaluating the impact of the insertion of linguistic information on the quality of the transcription.