Towards Robust Online Dialogue Response Generation
- URL: http://arxiv.org/abs/2203.03168v1
- Date: Mon, 7 Mar 2022 06:51:41 GMT
- Title: Towards Robust Online Dialogue Response Generation
- Authors: Leyang Cui, Fandong Meng, Yijin Liu, Jie Zhou, Yue Zhang
- Abstract summary: We argue that this can be caused by a discrepancy between training and real-world testing.
We propose a hierarchical sampling-based method consisting of both utterance-level sampling and semi-utterance-level sampling.
- Score: 62.99904593650087
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Although pre-trained sequence-to-sequence models have achieved great success
in dialogue response generation, chatbots still suffer from generating
inconsistent responses in real-world practice, especially in multi-turn
settings. We argue that this can be caused by a discrepancy between training
and real-world testing. At training time, chatbot generates the response with
the golden context, while it has to generate based on the context consisting of
both user utterances and the model predicted utterances during real-world
testing. With the growth of the number of utterances, this discrepancy becomes
more serious in the multi-turn settings. In this paper, we propose a
hierarchical sampling-based method consisting of both utterance-level sampling
and semi-utterance-level sampling, to alleviate the discrepancy, which
implicitly increases the dialogue coherence. We further adopt reinforcement
learning and re-ranking methods to explicitly optimize the dialogue coherence
during training and inference, respectively. Empirical experiments show the
effectiveness of the proposed methods for improving the robustness of chatbots
in real practice.
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