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

• URL: http://arxiv.org/abs/2604.10417v1
• Date: Sun, 12 Apr 2026 02:30:31 GMT
• Title: LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset
• Abstract summary: We construct the first Low-resource languages Aspect-based Sentiment Quadruple dataset, named LASQ.<n>It includes two low-resource languages: Uzbek and Uyghur.<n>It includes a fine-grained target-aspect-opinion-sentiment quadruple extraction task.
• Score: 23.766167086091315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction [22.152211131922943]
  We propose HYDRE -- HYbrid Distantly Supervised Relation Extraction framework.<n>It first uses a trained DSRE model to identify the top-k candidate relations for a given test sentence.<n>It then uses a novel dynamic exemplar retrieval strategy that extracts reliable, sentence-level exemplars from training data.
  arXiv  Detail & Related papers  (2025-10-21T06:55:19Z)
• LACA: Improving Cross-lingual Aspect-Based Sentiment Analysis with LLM Data Augmentation [0.8602553195689511]
  Cross-lingual aspect-based sentiment analysis involves detailed sentiment analysis in a target language.<n>Most existing methods depend heavily on often unreliable translation tools to bridge the language gap.<n>We propose a new approach that leverages a large language model to generate high-quality pseudo-labelled data in the target language.
  arXiv  Detail & Related papers  (2025-08-13T05:55:48Z)
• CrosGrpsABS: Cross-Attention over Syntactic and Semantic Graphs for Aspect-Based Sentiment Analysis in a Low-Resource Language [2.3157475769949616]
  Aspect-Based Sentiment Analysis (ABSA) is a fundamental task in natural language processing, offering fine-grained insights into opinions expressed in text.<n>This research propose CrosGrpsABS, a novel hybrid framework that leverages bidirectional cross-attention between syntactic and semantic graphs to enhance aspect-level sentiment classification.<n>We evaluate CrosGrpsABS on four low-resource Bengali ABSA datasets and the high-resource English SemEval 2014 Task 4 dataset.
  arXiv  Detail & Related papers  (2025-05-25T07:42:32Z)
• Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch [31.50694642284321]
  We propose a Bi-Confidence-Frequency Cross-Lingual transfer framework (BiCF) to tackle the obstacle of lacking low-resource language dialogue data. Our framework performs reliably and cost-efficiently on different scales of manually annotated Indonesian data.
  arXiv  Detail & Related papers  (2024-10-24T04:33:14Z)
• Cross-Lingual Word Alignment for ASEAN Languages with Contrastive Learning [5.5119571570277826]
  Cross-lingual word alignment plays a crucial role in various natural language processing tasks. Recent study proposes a BiLSTM-based encoder-decoder model that outperforms pre-trained language models in low-resource settings. We propose incorporating contrastive learning into the BiLSTM-based encoder-decoder framework.
  arXiv  Detail & Related papers  (2024-07-06T11:56:41Z)
• MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting [53.77590764277568]
  We introduce a novel MoE-CT architecture that separates the base model's learning from the multilingual expansion process. Our design freezes the original LLM parameters, thus safeguarding its performance in high-resource languages, while an appended MoE module, trained on diverse language datasets, augments low-resource language proficiency.
  arXiv  Detail & Related papers  (2024-06-25T11:03:45Z)
• TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale [66.01943465390548]
  We introduce TriSum, a framework for distilling large language models' text summarization abilities into a compact, local model. Our method enhances local model performance on various benchmarks. It also improves interpretability by providing insights into the summarization rationale.
  arXiv  Detail & Related papers  (2024-03-15T14:36:38Z)
• MoSECroT: Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer [50.40191599304911]
  We introduce MoSECroT Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer. In this paper, we present the first framework that leverages relative representations to construct a common space for the embeddings of a source language PLM and the static word embeddings of a target language. We show that although our proposed framework is competitive with weak baselines when addressing MoSECroT, it fails to achieve competitive results compared with some strong baselines.
  arXiv  Detail & Related papers  (2024-01-09T21:09:07Z)
• RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models [57.12888828853409]
  RAVEN is a model that combines retrieval-augmented masked language modeling and prefix language modeling. Fusion-in-Context Learning enables the model to leverage more in-context examples without requiring additional training. Our work underscores the potential of retrieval-augmented encoder-decoder language models for in-context learning.
  arXiv  Detail & Related papers  (2023-08-15T17:59:18Z)
• Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages [70.25418443146435]
  We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data. We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information. With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic.
  arXiv  Detail & Related papers  (2023-07-16T00:45:42Z)
• mFACE: Multilingual Summarization with Factual Consistency Evaluation [79.60172087719356]
  Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries. We leverage factual consistency evaluation models to improve multilingual summarization.
  arXiv  Detail & Related papers  (2022-12-20T19:52:41Z)
• DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis [84.80347062834517]
  We introduce DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We develop a neural model to benchmark the task, which advances in effectively performing end-to-end quadruple prediction.
  arXiv  Detail & Related papers  (2022-11-10T17:18:20Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.