Near-Zero-Shot Suggestion Mining with a Little Help from WordNet
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Abstract (arXiv)
In this work, we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues, services, and other items of interest. To reduce training costs and annotation efforts needed to build a classifier for a specific label set, we present and evaluate several entailment-based zero-shot approaches to suggestion classification in a label-fully-unseen fashion. In particular, we introduce the strategy of assigning target class labels to sentences in English language with user intentions, which significantly improves prediction quality. The proposed strategies are evaluated with a comprehensive experimental study that validated our results both quantitatively and qualitatively.
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References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
- 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
- 2015A large annotated corpus for learning natural language inferencecited by 1,847
- 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 308
- 2018Multinomial Adversarial Networks for Multi-Domain Text Classificationcited by 40
- 2017Leveraging Cognitive Features for Sentiment Analysiscited by 19
- 2019Language-Independent Sentiment Analysis Using Subjectivity and Positional Informationcited by 2
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