Text Entailment approach for Zero-shot Text Classification (Research Paper Walkthrough)

#zeroshot #textclassification #nlp
⏩ 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.

⏩ OUTLINE:
0:00 - Understanding Zero-shot Text Classification with example
01:45 - An entailment approach for zero-shot text classification
03:11 - Text entailment task
04:01 - Converting labels into hypothesis with example
07:32 - Results

⏩ Paper Title: Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach
⏩ Paper: aclanthology.org/D19-1404/
⏩ Author: Wenpeng Yin, Jamaal Hay, Dan Roth
⏩ Organisation: Cognitive Computation Group, Department of Computer and Information Science, University of Pennsylvania

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I am Prakhar Mishra and this channel is my passion project. I am currently pursuing my MS (by research) in Data Science. I have an industry work-ex of 3 years in the field of Data Science and Machine Learning with a particular focus on Natural Language Processing (NLP).