Computer Science > Computation and Language
[Submitted on 23 May 2022 (v1), last revised 15 Nov 2022 (this version, v2)]
Title:A Fine-grained Interpretability Evaluation Benchmark for Neural NLP
View PDFAbstract:While there is increasing concern about the interpretability of neural models, the evaluation of interpretability remains an open problem, due to the lack of proper evaluation datasets and metrics. In this paper, we present a novel benchmark to evaluate the interpretability of both neural models and saliency methods. This benchmark covers three representative NLP tasks: sentiment analysis, textual similarity and reading comprehension, each provided with both English and Chinese annotated data. In order to precisely evaluate the interpretability, we provide token-level rationales that are carefully annotated to be sufficient, compact and comprehensive. We also design a new metric, i.e., the consistency between the rationales before and after perturbations, to uniformly evaluate the interpretability on different types of tasks. Based on this benchmark, we conduct experiments on three typical models with three saliency methods, and unveil their strengths and weakness in terms of interpretability. We will release this benchmark this https URL and hope it can facilitate the research in building trustworthy systems.
Submission history
From: Lijie Wang [view email][v1] Mon, 23 May 2022 07:37:04 UTC (606 KB)
[v2] Tue, 15 Nov 2022 02:09:01 UTC (380 KB)
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