Summary of Aste Transformer Modelling Dependencies in Aspect-sentiment Triplet Extraction, by Iwo Naglik and Mateusz Lango
ASTE Transformer Modelling Dependencies in Aspect-Sentiment Triplet Extraction
by Iwo Naglik, Mateusz Lango
First submitted to arxiv on: 23 Sep 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 A recently proposed task in natural language processing is aspect-sentiment triplet extraction (ASTE), which involves identifying triples consisting of an aspect phrase, opinion phrase, and sentiment polarity from a given sentence. State-of-the-art methods typically approach this task by first extracting all possible text spans, then filtering potential aspect and opinion phrases using classifiers, and finally combining their pairs with another classifier that assigns sentiment polarity. However, these approaches rely on independent classifier decisions, preventing the exploitation of dependencies between extracted phrases and hindering the use of interrelationship knowledge to improve performance. This paper proposes a new ASTE approach based on three transformer-inspired layers, enabling the modeling of phrase dependencies and interrelationship-based predictions. Experimental results demonstrate that this method outperforms other approaches in terms of F1 measure on popular benchmarks, with a simple pre-training technique further improving model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ASTE is a task where we try to find what people like or dislike about something. Right now, the best way to do this involves several steps: first, finding all the important parts of the text, then filtering out the not-so-important ones, and finally deciding if the remaining phrases are positive, negative, or neutral. The problem with these approaches is that they don’t take into account how the different phrases relate to each other. This paper proposes a new way to do ASTE by using special kinds of computer models that can understand relationships between phrases. It also shows that this approach works better than others on popular tests. |
Keywords
» Artificial intelligence » Natural language processing » Transformer
