5,427 research outputs found
X-ReCoSa: Multi-Scale Context Aggregation For Multi-Turn Dialogue Generation
In multi-turn dialogue generation, responses are not only related to the
topic and background of the context but also related to words and phrases in
the sentences of the context. However, currently widely used hierarchical
dialog models solely rely on context representations from the utterance-level
encoder, ignoring the sentence representations output by the word-level
encoder. This inevitably results in a loss of information while decoding and
generating. In this paper, we propose a new dialog model X-ReCoSa to tackle
this problem which aggregates multi-scale context information for hierarchical
dialog models. Specifically, we divide the generation decoder into upper and
lower parts, namely the intention part and the generation part. Firstly, the
intention part takes context representations as input to generate the intention
of the response. Then the generation part generates words depending on sentence
representations. Therefore, the hierarchical information has been fused into
response generation. we conduct experiments on the English dataset DailyDialog.
Experimental results exhibit that our method outperforms baseline models on
both automatic metric-based and human-based evaluations
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
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