Adaptive Parameterization for Neural Dialogue Generation Article Swipe
Hengyi Cai
,
Hongshen Chen
,
Cheng Zhang
,
Yonghao Song
,
Xiaofang Zhao
,
Dawei Yin
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.18653/v1/d19-1188
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.18653/v1/d19-1188
Neural conversation systems generate responses based on the sequence-to-sequence (SEQ2SEQ) paradigm. Typically, the model is equipped with a single set of learned parameters to generate responses for given input contexts. When confronting diverse conversations, its adaptability is rather limited and the model is hence prone to generate generic responses. In this work, we propose an {\bf Ada}ptive {\bf N}eural {\bf D}ialogue generation model, \textsc{AdaND}, which manages various conversations with conversation-specific parameterization. For each conversation, the model generates parameters of the encoder-decoder by referring to the input context. In particular, we propose two adaptive parameterization mechanisms: a context-aware and a topic-aware parameterization mechanism. The context-aware parameterization directly generates the parameters by capturing local semantics of the given context. The topic-aware parameterization enables parameter sharing among conversations with similar topics by first inferring the latent topics of the given context and then generating the parameters with respect to the distributional topics. Extensive experiments conducted on a large-scale real-world conversational dataset show that our model achieves superior performance in terms of both quantitative metrics and human evaluations.
Related Topics
Concepts
Computer science
Conversation
Context (archaeology)
Adaptability
Set (abstract data type)
Semantics (computer science)
Sequence (biology)
Artificial intelligence
Context model
Encoder
Adaptation (eye)
Machine learning
Linguistics
Philosophy
Ecology
Object (grammar)
Operating system
Genetics
Optics
Programming language
Physics
Paleontology
Biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/d19-1188
- https://www.aclweb.org/anthology/D19-1188.pdf
- OA Status
- gold
- Cited By
- 9
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2977149219
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2977149219Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18653/v1/d19-1188Digital Object Identifier
- Title
-
Adaptive Parameterization for Neural Dialogue GenerationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Hengyi Cai, Hongshen Chen, Cheng Zhang, Yonghao Song, Xiaofang Zhao, Dawei YinList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/d19-1188Publisher landing page
- PDF URL
-
https://www.aclweb.org/anthology/D19-1188.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.aclweb.org/anthology/D19-1188.pdfDirect OA link when available
- Concepts
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Computer science, Conversation, Context (archaeology), Adaptability, Set (abstract data type), Semantics (computer science), Sequence (biology), Artificial intelligence, Context model, Encoder, Adaptation (eye), Machine learning, Linguistics, Philosophy, Ecology, Object (grammar), Operating system, Genetics, Optics, Programming language, Physics, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2021: 5, 2020: 3Per-year citation counts (last 5 years)
- References (count)
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50Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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