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Journal Article Sentence Model Based Subword Embeddings for a Dialog System
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Authors
Euisok Chung, Hyun Woo Kim, Hwa Jeon Song
Issue Date
2022-08
Citation
ETRI Journal, v.44, no.4, pp.599-612
ISSN
1225-6463
Publisher
한국전자통신연구원 (ETRI)
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.4218/etrij.2020-0245
Project Code
20ZS1100, Core Technology Research for Self-Improving Integrated Artificial Intelligence System, Hwa Jeon Song
Abstract
This study focuses on improving a word embedding model to enhance the performance of downstream tasks, such as those of dialog systems. To improve traditional word embedding models, such as skip-gram, it is critical to refine the word features and expand the context model. In this paper, we approach the word model from the perspective of subword embedding and attempt to extend the context model by integrating various sentence models. Our proposed sentence model is a subword-based skip-thought model that integrates self-attention and relative position encoding techniques. We also propose a clustering-based dialog model for downstream task verification and evaluate its relationship with the sentence-model-based subword embedding technique. The proposed subword embedding method produces better results than previous methods in evaluating word and sentence similarity. In addition, the downstream task verification, a clustering-based dialog system, demonstrates an improvement of up to 4.86% over the results of FastText in previous research.
KSP Keywords
Context model, Dialog system, Embedding Technique, Encoding Technique, Relative position, Sentence Similarity, Sentence model, Thought model, Word embedding models, clustering based, embedding method
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
Type 4: