Graph-Based Semi-Supervised Learning for Natural Language Understanding

Zimeng Qiu, Eunah Cho, Xiaochun Ma, William Campbell


Abstract
Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited. In this work, we propose transductive graph-based semi-supervised learning models as well as their inductive variants for NLU. We evaluate the approach’s applicability using publicly available NLU data and models. In order to find similar utterances and construct a graph, we use a paraphrase detection model. Results show that applying the inductive graph-based semi-supervised learning can improve the error rate of the NLU model by 5%.
Anthology ID:
D19-5318
Volume:
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | TextGraphs | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
151–158
Language:
URL:
https://aclanthology.org/D19-5318
DOI:
10.18653/v1/D19-5318
Bibkey:
Cite (ACL):
Zimeng Qiu, Eunah Cho, Xiaochun Ma, and William Campbell. 2019. Graph-Based Semi-Supervised Learning for Natural Language Understanding. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 151–158, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
Graph-Based Semi-Supervised Learning for Natural Language Understanding (Qiu et al., EMNLP 2019)
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PDF:
https://aclanthology.org/D19-5318.pdf
Attachment:
 D19-5318.Attachment.zip