Learning to Embed Semantic Correspondence for Natural Language Understanding

Sangkeun Jung, Jinsik Lee, Jiwon Kim


Abstract
While learning embedding models has yielded fruitful results in several NLP subfields, most notably Word2Vec, embedding correspondence has relatively not been well explored especially in the context of natural language understanding (NLU), a task that typically extracts structured semantic knowledge from a text. A NLU embedding model can facilitate analyzing and understanding relationships between unstructured texts and their corresponding structured semantic knowledge, essential for both researchers and practitioners of NLU. Toward this end, we propose a framework that learns to embed semantic correspondence between text and its extracted semantic knowledge, called semantic frame. One key contributed technique is semantic frame reconstruction used to derive a one-to-one mapping between embedded vectors and their corresponding semantic frames. Embedding into semantically meaningful vectors and computing their distances in vector space provides a simple, but effective way to measure semantic similarities. With the proposed framework, we demonstrate three key areas where the embedding model can be effective: visualization, semantic search and re-ranking.
Anthology ID:
K18-1013
Volume:
Proceedings of the 22nd Conference on Computational Natural Language Learning
Month:
October
Year:
2018
Address:
Brussels, Belgium
Editors:
Anna Korhonen, Ivan Titov
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
131–140
Language:
URL:
https://aclanthology.org/K18-1013
DOI:
10.18653/v1/K18-1013
Bibkey:
Cite (ACL):
Sangkeun Jung, Jinsik Lee, and Jiwon Kim. 2018. Learning to Embed Semantic Correspondence for Natural Language Understanding. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 131–140, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Learning to Embed Semantic Correspondence for Natural Language Understanding (Jung et al., CoNLL 2018)
Copy Citation:
PDF:
https://aclanthology.org/K18-1013.pdf