Abstract
Named entity recognition (NER) is a fundamental task in natural language processing. Recently, NER has been formulated as a machine reading comprehension (MRC) task, in which manually-crafted queries are used to extract entities of different types. However, current MRC-based NER techniques are limited to extracting a single type of entities at a time and are largely geared towards resource-rich settings. This renders them inefficient during the inference phase, while also leaving their potential untapped for utilization in low-resource settings. We suggest a query-parallel MRC-based approach to address these issues, which is capable of extracting multiple entity types concurrently and is applicable to both resource-rich and resource-limited settings. Specifically, we propose a query-parallel encoder which uses a query-segmented attention mechanism to isolate the semantics of queries and model the query-context interaction with a unidirectional flow. This allows for easier generalization to new entity types or transfer to new domains. After obtaining the query and context representations through the encoder, they are fed into a query-conditioned biaffine predictor to extract multiple entities at once. The model is trained with parameter-efficient tuning technique, making it more data-efficient. We conduct extensive experiments and demonstrate that our model performs competitively against strong baseline methods in resource-rich settings, and achieves state-of-the-art results in low-resource settings, including training-from-scratch, in-domain transfer and cross-domain transfer tasks.- Anthology ID:
- 2023.findings-emnlp.135
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2052–2065
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.135
- DOI:
- 10.18653/v1/2023.findings-emnlp.135
- Cite (ACL):
- Yuhao Zhang and Yongliang Wang. 2023. A Query-Parallel Machine Reading Comprehension Framework for Low-resource NER. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2052–2065, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- A Query-Parallel Machine Reading Comprehension Framework for Low-resource NER (Zhang & Wang, Findings 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.findings-emnlp.135.pdf