3,837 research outputs found
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
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Natural language generation (NLG) is a critical component of spoken dialogue
and it has a significant impact both on usability and perceived quality. Most
NLG systems in common use employ rules and heuristics and tend to generate
rigid and stylised responses without the natural variation of human language.
They are also not easily scaled to systems covering multiple domains and
languages. This paper presents a statistical language generator based on a
semantically controlled Long Short-term Memory (LSTM) structure. The LSTM
generator can learn from unaligned data by jointly optimising sentence planning
and surface realisation using a simple cross entropy training criterion, and
language variation can be easily achieved by sampling from output candidates.
With fewer heuristics, an objective evaluation in two differing test domains
showed the proposed method improved performance compared to previous methods.
Human judges scored the LSTM system higher on informativeness and naturalness
and overall preferred it to the other systems.Comment: To be appear in EMNLP 201
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
The natural language generation (NLG) component of a spoken dialogue system
(SDS) usually needs a substantial amount of handcrafting or a well-labeled
dataset to be trained on. These limitations add significantly to development
costs and make cross-domain, multi-lingual dialogue systems intractable.
Moreover, human languages are context-aware. The most natural response should
be directly learned from data rather than depending on predefined syntaxes or
rules. This paper presents a statistical language generator based on a joint
recurrent and convolutional neural network structure which can be trained on
dialogue act-utterance pairs without any semantic alignments or predefined
grammar trees. Objective metrics suggest that this new model outperforms
previous methods under the same experimental conditions. Results of an
evaluation by human judges indicate that it produces not only high quality but
linguistically varied utterances which are preferred compared to n-gram and
rule-based systems.Comment: To be appear in SigDial 201
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