ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술지 Domain-Slot Relationship Modeling Using a Pre-Trained Language Encoder for Multi-Domain Dialogue State Tracking
Cited 2 time in scopus Download 3 time Share share facebook twitter linkedin kakaostory
저자
안진원, 조성준, 방준성, 김미숙
발행일
202206
출처
IEEE/ACM Transactions on Audio, Speech, and Language Processing, v.30, pp.2091-2102
ISSN
2329-9290
출판사
ACM, IEEE
DOI
https://dx.doi.org/10.1109/TASLP.2022.3181350
협약과제
22IR1600, 대화형 치안 지식 서비스 폴봇 개발, 방준성
초록
Dialogue state tracking for multi-domain dialogues is challenging because the model should be able to track dialogue states across multiple domains and slots. As using pre-trained language models is the de facto standard for natural language processing tasks, many recent studies use them to encode the dialogue context for predicting the dialogue states. Model architectures that have certain inductive biases for modeling the relationship among different domain-slot pairs are also emerging. Our work is based on these research approaches on multi-domain dialogue state tracking. We propose a model architecture that effectively models the relationship among domain-slot pairs using a pre-trained language encoder. Inspired by the way the special [CLS] token in BERT is used to aggregate the information of the whole sequence, we use multiple special tokens for each domain-slot pair that encodes information corresponding to its domain and slot. The special tokens are run together with the dialogue context through the pre-trained language encoder, which effectively models the relationship among different domain-slot pairs. Our experimental results on the datasets MultiWOZ-2.0 and MultiWOZ-2.1 show that our model outperforms other models with the same setting. Our ablation studies incorporate three main parts. The first component shows the effectiveness of our approach exploiting the relationship modeling. The second component compares the effect of using different pre-trained language encoders. The final component involves comparing different initialization methods that could be used for the special tokens. Qualitative analysis of the attention map of the pre-trained language encoder shows that our special tokens encode relevant information through the encoding process by attending to each other.
KSP 제안 키워드
De facto standard, Initialization methods, Language model, Model architecture, Multi-Domain, Multiple domains, Natural Language Processing, Qualitative analysis, Relationship Modeling, State tracking, encoding process