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LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset

arXiv 2604.10417 ↗First posted 12 Apr 2026cs.CLcited by 0

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Abstract (arXiv)

In recent years, aspect-based sentiment analysis (ABSA) has made rapid progress and shown strong practical value. However, existing research and benchmarks are largely concentrated on high-resource languages, leaving fine-grained sentiment extraction in low-resource languages under-explored. To address this gap, we constructed the first Low-resource languages Aspect-based Sentiment Quadruple dataset, named LASQ, which includes two low-resource languages: Uzbek and Uyghur. Secondly, it includes a fine-grained target-aspect-opinion-sentiment quadruple extraction task. To facilitate future research, we designed a grid-tagging model that integrates syntactic knowledge. This model incorporates part-of-speech (POS) and dependency knowledge into the model through our designed Syntax Knowledge Embedding Module (SKEM), thereby alleviating the lexical sparsity problem caused by agglutinative languages. Experiments on LASQ demonstrate consistent gains over competitive baselines, validating both the dataset's utility and the effectiveness of the proposed modeling approach.

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References · 25

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
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  5. 2019ERNIE 2.0: A Continual Pre-training Framework for Language Understandingcited by 231
  6. 2019Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysiscited by 123
  7. 2021Aspect Sentiment Quad Prediction as Paraphrase Generationcited by 100
  8. 2017Interactive Attention Networks for Aspect-Level Sentiment Classificationcited by 91
  9. 2024Why do language models perform worse for morphologically complex languages?cited by 62
  10. 2021Learning Span-Level Interactions for Aspect Sentiment Triplet Extractioncited by 60
  11. 2018A Unified Model for Opinion Target Extraction and Target Sentiment Predictioncited by 56
  12. 2021Syntax-BERT: Improving Pre-trained Transformers with Syntax Treescited by 51
13 more
  1. 2019Attentional Encoder Network for Targeted Sentiment Classificationcited by 48
  2. 2020Efficient Second-Order TreeCRF for Neural Dependency Parsingcited by 48
  3. 2022DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysiscited by 27
  4. 2021Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognitioncited by 18
  5. 2023Large language models for aspect-based sentiment analysiscited by 17
  6. 2020Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extractioncited by 12
  7. 2024Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategycited by 12
  8. 2019Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysiscited by 9
  9. 2022Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecturecited by 7
  10. 2024A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysiscited by 5
  11. 2021Fine-tuning Pretrained Multilingual BERT Model for Indonesian Aspect-based Sentiment Analysiscited by 4
  12. 2025Multi-Domain ABSA Conversation Dataset Generation via LLMs for Real-World Evaluation and Model Comparisoncited by 2
  13. 2025From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Modelscited by 1

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