Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis
Not on the map. Why?
Abstract (arXiv)
In the domain of Aspect-Based Sentiment Analysis (ABSA), generative methods have shown promising results and achieved substantial advancements. However, despite these advancements, the tasks of extracting sentiment quadruplets, which capture the nuanced sentiment expressions within a sentence, remain significant challenges. In particular, compound sentences can potentially contain multiple quadruplets, making the extraction task increasingly difficult as sentence complexity grows. To address this issue, we are focusing on simplifying sentence structures to facilitate the easier recognition of these elements and crafting a model that integrates seamlessly with various ABSA tasks. In this paper, we propose Aspect Term Oriented Sentence Splitter (ATOSS), which simplifies compound sentence into simpler and clearer forms, thereby clarifying their structure and intent. As a plug-and-play module, this approach retains the parameters of the ABSA model while making it easier to identify essential intent within input sentences. Extensive experimental results show that utilizing ATOSS outperforms existing methods in both ASQP and ACOS tasks, which are the primary tasks for extracting sentiment quadruplets.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 3 Oct 2024.
Builds on
14 of 25 references.
References · 14
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2015Distilling the Knowledge in a Neural Networkcited by 10,076
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
- 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 710
- 2022Large Language Models Are Reasoning Teacherscited by 318
- 2022Teaching Small Language Models to Reasoncited by 279
- 2019Assessing Social and Intersectional Biases in Contextualized Word Representationscited by 134
- 2019Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysiscited by 123
- 2021Aspect Sentiment Quad Prediction as Paraphrase Generationcited by 99
- 2023MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Predictioncited by 49
Built on by
Citing papers per year: 2025: 2.
2 papers.