Assessing Word Importance Using Models Trained for Semantic Tasks
Dávid Javorský Ondřej Bojar François Yvon
Many NLP tasks require to automatically identify the most significant words
in a text. In this work, we derive word significance from models trained to
solve semantic task: Natural Language Inference and Paraphrase Identification.
Using an attribution method aimed to explain the predictions of these models,
we derive importance scores for each input token. We evaluate their relevance
using a so-called cross-task evaluation: Analyzing the performance of one model
on an input masked according to the other model's weight, we show that our
method is robust with respect to the choice of the initial task. Additionally,
we investigate the scores from the syntax point of view and observe interesting
patterns, e.g. words closer to the root of a syntactic tree receive higher
importance scores. Altogether, these observations suggest that our method can
be used to identify important words in sentences without any explicit word
importance labeling in training.