Enhancing Chinese Dialogue Generation with Word–Phrase Fusion Embedding and Sparse SoftMax Optimization Article Swipe
Shenrong Lv
,
Siyu Lu
,
Ruiyang Wang
,
Lirong Yin
,
Zhengtong Yin
,
Salman A. AlQahtani
,
Jiawei Tian
,
Wenfeng Zheng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/systems12120516
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/systems12120516
Chinese dialogue generation faces multiple challenges, such as semantic understanding, information matching, and response fluency. Generative dialogue systems for Chinese conversation are somehow difficult to construct because of the flexible word order, the great impact of word replacement on semantics, and the complex implicit context. Existing methods still have limitations in addressing these issues. To tackle these problems, this paper proposes an improved Chinese dialogue generation model based on transformer architecture. The model uses a multi-layer transformer decoder as the backbone and introduces two key techniques, namely incorporating pre-trained language model word embeddings and optimizing the sparse Softmax loss function. For word-embedding fusion, we concatenate the word vectors from the pre-trained model with character-based embeddings to enhance the semantic information of word representations. The sparse Softmax optimization effectively mitigates the overfitting issue by introducing a sparsity regularization term. Experimental results on the Chinese short text conversation (STC) dataset demonstrate that our proposed model significantly outperforms the baseline models on automatic evaluation metrics, such as BLEU and Distinct, with an average improvement of 3.5 percentage points. Human evaluations also validate the superiority of our model in generating fluent and relevant responses. This work provides new insights and solutions for building more intelligent and human-like Chinese dialogue systems.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/systems12120516
- https://www.mdpi.com/2079-8954/12/12/516/pdf?version=1732445711
- OA Status
- gold
- Cited By
- 8
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404695089
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404695089Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/systems12120516Digital Object Identifier
- Title
-
Enhancing Chinese Dialogue Generation with Word–Phrase Fusion Embedding and Sparse SoftMax OptimizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-24Full publication date if available
- Authors
-
Shenrong Lv, Siyu Lu, Ruiyang Wang, Lirong Yin, Zhengtong Yin, Salman A. AlQahtani, Jiawei Tian, Wenfeng ZhengList of authors in order
- Landing page
-
https://doi.org/10.3390/systems12120516Publisher landing page
- PDF URL
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https://www.mdpi.com/2079-8954/12/12/516/pdf?version=1732445711Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-8954/12/12/516/pdf?version=1732445711Direct OA link when available
- Concepts
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Softmax function, Embedding, Phrase, Computer science, Natural language processing, Artificial intelligence, Word (group theory), Word embedding, Fusion, Speech recognition, Linguistics, Deep learning, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2025: 8Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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