Summary of Roast: Review-level Opinion Aspect Sentiment Target Joint Detection For Absa, by Siva Uday Sampreeth Chebolu and Franck Dernoncourt and Nedim Lipka and Thamar Solorio
ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA
by Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, Thamar Solorio
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel task in Aspect-Based Sentiment Analysis (ABSA) called Review-Level Opinion Aspect Sentiment Target (ROAST), which aims to identify every ABSA constituent at the review level, closing the gap between sentence-level and text-level ABSA. The task is designed to enable ABSA research to cover more ground and gain a deeper understanding of the task’s practical application in various languages and domains. To achieve this, the paper extends existing datasets to include low-resource languages, multiple languages, and diverse topics, addressing previous research shortcomings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ABSA has grown rapidly due to shared tasks across languages and fields. However, some gaps remain, such as lacking evaluations for low-resource languages and focusing on sentence-level analysis. To address this, the ROAST task identifies ABSA constituents at the review level, closing the gap between sentence-level and text-level ABSA. The paper extends datasets to include low-resource languages, multiple languages, and diverse topics. |
