Log- FGAER : Logic-Guided Fine-Grained Address Entity Recognition from Multi-Turn Spoken Dialogue (2023)

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.

## introduction fine-grained address entity recognition from calls is an important task in many applications @xcite , such as obtaining delivery addresses from e-commerce assistant or post-sale service @xcite . to be specific, the scenario considered in this paper is the extraction of fine-grained address entities distributed through the multi-turn spoken dialogue contexts. figure 1 depicts an example of such a dialogue between a customer and an after-service call center in which the fine-grained address entities are recognized and then typically combined to form a full standard address for further delivery. existing name entity recognition (ner) methods usually combine the pre-trained language models (e.g. bert) with supervised models such as bilstm/lstm-crf @xcite @xcite or with some expansive external knowledge base @xcite . these methods, which are trained and evaluated on text from well-written sources, do not perform well in a spoken dialogue context, because the entities in the spoken scenario are distributed across a multi-turn context with a lot of noise. other studies explore the ner problem in a spoken or dialogue context @xcite @xcite , but their labels are not applicable to the address domain. because of the diversity and complexity of multi-turn spoken dialogue scenarios, annotating for the fgaer task is laborious and time-consuming @xcite . to address these issues, we propose a logicguided fine-grained address entity recognition method (log-fgaer). the log-fgaer is based on the fact that address entities follow spatially constrained relationships @xcite . as a result, the multi-turn spoken dialogue must maintain a discourse structure to get the full standard address for the duration of address acquiring in accordance with the spatial constraint relationship between the geographical address entities. figure 1(a) shows an example of a 4-level spatial dependency tree structure, in which each node is a fine-grained address entity, each level represents an entity type (e.g. "city" and "district"), and each edge is a dependency relation between the entities. a full standard address is made up of nodes in the path from the root to the leaf node. we can improve entity recognition inference by presenting such an interdependent hierarchy relationship as the logic rule and then using probabilistic soft logic (psl) to regulate the loss objective. where are you in suzhou city, jiangsu prov? 您现在在江苏 苏州市 的哪里？ i'm in wujiang district, shengze town in wujiang district. ## conclusion in this paper, we propose an effective method named log-fgaer to extract address entities from spoken dialogue contexts using the psl rule. leveraging the spatially constrained relationship of address entities in the dialogues, we improve entity recognition inference by presenting the interdependent hierarchy relationship as the logic rule to regulate the loss objective. we also propose a data augmentation paradigm to construct the labeled multi-turn dialogue dataset for training and evaluation of our proposed method. experiments on the multi-turn spoken dialogue dataset show the effectiveness of our method.