Comments (1)
As proposed by the authors in CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction, current leading methods can be categorized mainly into five distinct groups in which I think generative ones are great for the case of latent aspect detection with no surface form:
- Pipeline
- Tagging-based
- Span-based
- Generative
- MRC-based
Among generative ones, one group of the methods is based on BERT LM, and the other group is based on T5 LM.
*This paper has been published in EMNLP'23 but has not become available yet, so I provided the link from Arxiv.
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Related Issues (20)
- batch execution of nllb translation
- Distribution of aspect terms and aspect categories in datasets HOT 6
- Adding HAST as a supervised baseline HOT 10
- Classification baseline for aspect term extraction HOT 18
- pipeline progress flow
- Check the existing readme and codeline HOT 2
- Gif image/video for illustrating the pipeline HOT 2
- Dockerize and fix installation on linux HOT 13
- a server for the web app
- Setup and Quickstart HOT 5
- Aspect based sentiment analysis + Running Bert and Cat library
- Adding Twitter Reviews Dataset HOT 1
- Needing for update in OCTIS library HOT 2
- Aspect Sentiment Triplet Extraction Baseline HOT 20
- New baseline for Aspect-Based Sentiment Analysis HOT 2
- Adding a new tanslation model to the pipeline HOT 1
- OCTIS.CTM throws a value error during the training phase HOT 3
- Updating stats on quality of translation HOT 4
- Incorporating Underrepresented Languages: A Focus on Low-Resource Languages HOT 1
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