1,596 research outputs found
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation
Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in
building end-to-end trainable dialogue systems. Though highly efficient in
learning the backbone of human-computer communications, they suffer from the
problem of strongly favoring short generic responses. In this paper, we argue
that a good response should smoothly connect both the preceding dialogue
history and the following conversations. We strengthen this connection through
mutual information maximization. To sidestep the non-differentiability of
discrete natural language tokens, we introduce an auxiliary continuous code
space and map such code space to a learnable prior distribution for generation
purpose. Experiments on two dialogue datasets validate the effectiveness of our
model, where the generated responses are closely related to the dialogue
context and lead to more interactive conversations.Comment: Accepted by EMNLP201
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
While recent neural encoder-decoder models have shown great promise in
modeling open-domain conversations, they often generate dull and generic
responses. Unlike past work that has focused on diversifying the output of the
decoder at word-level to alleviate this problem, we present a novel framework
based on conditional variational autoencoders that captures the discourse-level
diversity in the encoder. Our model uses latent variables to learn a
distribution over potential conversational intents and generates diverse
responses using only greedy decoders. We have further developed a novel variant
that is integrated with linguistic prior knowledge for better performance.
Finally, the training procedure is improved by introducing a bag-of-word loss.
Our proposed models have been validated to generate significantly more diverse
responses than baseline approaches and exhibit competence in discourse-level
decision-making.Comment: Appeared in ACL2017 proceedings as a long paper. Correct a
calculation mistake in Table 1 E-bow & A-bow and results into higher score
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