1,972 research outputs found
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
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
Deep Reinforcement Learning for Dialogue Generation
Recent neural models of dialogue generation offer great promise for
generating responses for conversational agents, but tend to be shortsighted,
predicting utterances one at a time while ignoring their influence on future
outcomes. Modeling the future direction of a dialogue is crucial to generating
coherent, interesting dialogues, a need which led traditional NLP models of
dialogue to draw on reinforcement learning. In this paper, we show how to
integrate these goals, applying deep reinforcement learning to model future
reward in chatbot dialogue. The model simulates dialogues between two virtual
agents, using policy gradient methods to reward sequences that display three
useful conversational properties: informativity (non-repetitive turns),
coherence, and ease of answering (related to forward-looking function). We
evaluate our model on diversity, length as well as with human judges, showing
that the proposed algorithm generates more interactive responses and manages to
foster a more sustained conversation in dialogue simulation. This work marks a
first step towards learning a neural conversational model based on the
long-term success of dialogues
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