Improving Slot Filling by Utilizing Contextual Information

Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen


Abstract
Slot Filling (SF) is one of the sub-tasks of Spoken Language Understanding (SLU) which aims to extract semantic constituents from a given natural language utterance. It is formulated as a sequence labeling task. Recently, it has been shown that contextual information is vital for this task. However, existing models employ contextual information in a restricted manner, e.g., using self-attention. Such methods fail to distinguish the effects of the context on the word representation and the word label. To address this issue, in this paper, we propose a novel method to incorporate the contextual information in two different levels, i.e., representation level and task-specific (i.e., label) level. Our extensive experiments on three benchmark datasets on SF show the effectiveness of our model leading to new state-of-the-art results on all three benchmark datasets for the task of SF.
Anthology ID:
2020.nlp4convai-1.11
Volume:
Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | NLP4ConvAI | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
90–95
Language:
URL:
https://aclanthology.org/2020.nlp4convai-1.11
DOI:
10.18653/v1/2020.nlp4convai-1.11
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.nlp4convai-1.11.pdf
Video:
 http://slideslive.com/38929627