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MixRED: A Mix-lingual Relation Extraction Dataset

arXiv 2403.15696 ↗First posted 23 Mar 2024cs.AI cs.CLcited by 0

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Abstract (arXiv)

Relation extraction is a critical task in the field of natural language processing with numerous real-world applications. Existing research primarily focuses on monolingual relation extraction or cross-lingual enhancement for relation extraction. Yet, there remains a significant gap in understanding relation extraction in the mix-lingual (or code-switching) scenario, where individuals intermix contents from different languages within sentences, generating mix-lingual content. Due to the lack of a dedicated dataset, the effectiveness of existing relation extraction models in such a scenario is largely unexplored. To address this issue, we introduce a novel task of considering relation extraction in the mix-lingual scenario called MixRE and constructing the human-annotated dataset MixRED to support this task. In addition to constructing the MixRED dataset, we evaluate both state-of-the-art supervised models and large language models (LLMs) on MixRED, revealing their respective advantages and limitations in the mix-lingual scenario. Furthermore, we delve into factors influencing model performance within the MixRE task and uncover promising directions for enhancing the performance of both supervised models and LLMs in this novel task.

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References · 15

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2023Baichuan 2: Open Large-scale Language Modelscited by 664
  4. 2018Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Constructioncited by 321
  5. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  6. 2019DocRED: A Large-Scale Document-Level Relation Extraction Datasetcited by 239
  7. 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130
  8. 2020Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Poolingcited by 115
  9. 2017Joint Extraction of Entities and Relations Based on a Novel Tagging Schemecited by 102
  10. 2023Revisiting Relation Extraction in the era of Large Language Modelscited by 100
  11. 2020Reasoning with Latent Structure Refinement for Document-Level Relation Extractioncited by 97
  12. 2019Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentencescited by 55
3 more
  1. 2022Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysiscited by 30
  2. 2021Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triplescited by 13
  3. 2019Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mappingcited by 12

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