mlsanityData to 7 Oct 2026

Zero-Shot Aspect-Based Sentiment Analysis

arXiv 2202.01924 ↗First posted 4 Feb 2022cs.CL cs.AIcited by 5

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Abstract (arXiv)

Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated data for supervised training/fine-tuning. It is a big challenge to scale ABSA to a large number of new domains. This paper aims to train a unified model that can perform zero-shot ABSA without using any annotated data for a new domain. We propose a method called contrastive post-training on review Natural Language Inference (CORN). Later ABSA tasks can be cast into NLI for zero-shot transfer. We evaluate CORN on ABSA tasks, ranging from aspect extraction (AE), aspect sentiment classification (ASC), to end-to-end aspect-based sentiment analysis (E2E ABSA), which show ABSA can be conducted without any human annotated ABSA data.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 32 of its 51 references that are in the map. First posted on arXiv 4 Feb 2022.

Builds on

32 of 51 references.

References · 32

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20 more
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 1.

5 papers.

  1. 2023What do Language Models know about word senses? Zero-Shot WSD with Language Models and Domain Inventoriescited by 9
  2. 2024Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysiscited by 3
  3. 2022Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Compositioncited by 1
  4. 2023A Weak Supervision Approach for Few-Shot Aspect Based Sentimentcited by 1
  5. 2023ForeSeer: Product Aspect Forecasting Using Temporal Graph Embeddingcited by 1