Cross-lingual Bi-directional Propagation Model for Joint Intent Detection and Slot Filling
The goal of question understanding is to identify the underlying intent of a given utterance and extract all relevant slot labels in a question-answering system.Most traditional methods construct joint task models using a single-language corpus,disregarding the fact that user queries in real-world scenarios are often multilingual and diverse.Therefore,the current state-of-the-art methods lack ef-fective approaches to support multilingual joint intent detection and slot filling.In this paper,we propose a novel question understanding joint model called the Cross-lingual Bi-directional Propagation Model(XBPM),which focuses on enhancing the recognition performance of the model in multilin-gual scenarios,particularly in the context of Chinese ethnic minority languages.The proposed model leverages bi-directional connections between intent detection and slot filling tasks based on cross-lin-gual pre-training models,endowing it with strong cross-lingual transferability.Additionally,we con-struct a multilingual question understanding joint task corpus called XTFQD,which includes utter-ances in the tourism domain in both Chinese and Kazakh languages,addressing the data scarcity issue in multilingual question understanding joint tasks for ethnic minority languages.Comparative experi-mental results demonstrate that our model outperforms traditional joint models in terms of cross-lingual transfer performance.Further ablation experiments confirm the effectiveness of the proposed ap-proach.
cross-lingual transferintent detectionslot fillingquestion answering system
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