Diverse dialogue generation with context dependent dynamic loss function (2020.coling-main)
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| Challenge: | Dialogue systems using deep learning have achieved generation of fluent response sentences to user utterances, but they tend to produce responses that are not diverse and less context-dependent. |
| Approach: | They propose an Inverse N-gram loss function which incorporates contextual fluency and diversity at the same time by a simple formula. |
| Outcome: | The proposed loss function outperforms baseline models in automatic evaluations such as DIST-N and ROUGE and achieves higher scores on human evaluations of coherence and richness. |
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| Challenge: | Existing approaches to generate conversational dialogue produce uninteresting, predictable responses. |
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| Challenge: | Unlikelihood is a technique developed for removal of repetition in language model completions . it allows for a model to be generalized to solve a number of problems . |
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| Challenge: | Existing studies have tried to introduce discrete or Gaussian-based latent variables to address the one-to-many problem, but the diversity is limited. |
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Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue (2020.findings-emnlp)
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WeiSheng Zhang, Kaisong Song, Yangyang Kang, Zhongqing Wang, Changlong Sun, Xiaozhong Liu, Shoushan Li, Min Zhang, Luo Si
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| Challenge: | Existing methods to reduce question-related bias in video-grounded dialogue generation (VDG) however, the dataset often contains inherent bias, which can cause VDG models to learn spurious correlations between questions and answers. |
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