Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

DOI10.48550/ARXIV.1909.00161arXiv1909.00161MaRDI QIDQ109029

Author name not available (Why is that?)

Publication date: 31 August 2019

Abstract: Zero-shot text classification (0Shot-TC) is a challenging NLU problem to which little attention has been paid by the research community. 0Shot-TC aims to associate an appropriate label with a piece of text, irrespective of the text domain and the aspect (e.g., topic, emotion, event, etc.) described by the label. And there are only a few articles studying 0Shot-TC, all focusing only on topical categorization which, we argue, is just the tip of the iceberg in 0Shot-TC. In addition, the chaotic experiments in literature make no uniform comparison, which blurs the progress. This work benchmarks the 0Shot-TC problem by providing unified datasets, standardized evaluations, and state-of-the-art baselines. Our contributions include: i) The datasets we provide facilitate studying 0Shot-TC relative to conceptually different and diverse aspects: the ``topic aspect includes ``sports and ``politics as labels; the ``emotion aspect includes ``joy and ``anger; the ``situation aspect includes ``medical assistance and ``water shortage. ii) We extend the existing evaluation setup (label-partially-unseen) -- given a dataset, train on some labels, test on all labels -- to include a more challenging yet realistic evaluation label-fully-unseen 0Shot-TC (Chang et al., 2008), aiming at classifying text snippets without seeing task specific training data at all. iii) We unify the 0Shot-TC of diverse aspects within a textual entailment formulation and study it this way. Code & Data: https://github.com/yinwenpeng/BenchmarkingZeroShot

This page was built for publication: Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q109029)