Log-FGAER: Logic-Guided Fine-Grained Address Entity Recognition from Multi-Turn Spoken Dialogue

Xue Han, Yitong Wang, Qian Hu, Pengwei Hu, Chao Deng, Junlan Feng


Abstract
Fine-grained address entity recognition (FGAER) from multi-turn spoken dialogues is particularly challenging. The major reason lies in that a full address is often formed through a conversation process. Different parts of an address are distributed through multiple turns of a dialogue with spoken noises. It is nontrivial to extract by turn and combine them. This challenge has not been well emphasized by main-stream entity extraction algorithms. To address this issue, we propose in this paper a logic-guided fine-grained address recognition method (Log-FGAER), where we formulate the address hierarchy relationship as the logic rule and softly apply it in a probabilistic manner to improve the accuracy of FGAER. In addition, we provide an ontology-based data augmentation methodology that employs ChatGPT to augment a spoken dialogue dataset with labeled address entities. Experiments are conducted using datasets generated by the proposed data augmentation technique and derived from real-world scenarios. The results of the experiment demonstrate the efficacy of our proposal.
Anthology ID:
2023.emnlp-main.432
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6988–6997
Language:
URL:
https://aclanthology.org/2023.emnlp-main.432
DOI:
10.18653/v1/2023.emnlp-main.432
Bibkey:
Cite (ACL):
Xue Han, Yitong Wang, Qian Hu, Pengwei Hu, Chao Deng, and Junlan Feng. 2023. Log-FGAER: Logic-Guided Fine-Grained Address Entity Recognition from Multi-Turn Spoken Dialogue. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6988–6997, Singapore. Association for Computational Linguistics.
Cite (Informal):
Log-FGAER: Logic-Guided Fine-Grained Address Entity Recognition from Multi-Turn Spoken Dialogue (Han et al., EMNLP 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-2/2023.emnlp-main.432.pdf
Video:
 https://preview.aclanthology.org/nschneid-patch-2/2023.emnlp-main.432.mp4