Learning from Easy to Complex: Adaptive Multi-Curricula Learning for Neural Dialogue Generation Article Swipe
Hengyi Cai
,
Hongshen Chen
,
Cheng Zhang
,
Yonghao Song
,
Xiaofang Zhao
,
Yangxi Li
,
Dongsheng Duan
,
Dawei Yin
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v34i05.6244
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v34i05.6244
Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes—specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v34i05.6244
- https://ojs.aaai.org/index.php/AAAI/article/download/6244/6100
- OA Status
- diamond
- Cited By
- 28
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2997657234
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- OpenAlex ID
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https://openalex.org/W2997657234Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v34i05.6244Digital Object Identifier
- Title
-
Learning from Easy to Complex: Adaptive Multi-Curricula Learning for Neural Dialogue GenerationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-03Full publication date if available
- Authors
-
Hengyi Cai, Hongshen Chen, Cheng Zhang, Yonghao Song, Xiaofang Zhao, Yangxi Li, Dongsheng Duan, Dawei YinList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v34i05.6244Publisher landing page
- PDF URL
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https://ojs.aaai.org/index.php/AAAI/article/download/6244/6100Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/6244/6100Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Curriculum, Reinforcement learning, Process (computing), Converse, Adaptation (eye), Machine learning, Psychology, Mathematics, Operating system, Neuroscience, Pedagogy, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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28Total citation count in OpenAlex
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2025: 1, 2024: 3, 2023: 2, 2022: 5, 2021: 11Per-year citation counts (last 5 years)
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50Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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