1,758 research outputs found
Sequential Dialogue Context Modeling for Spoken Language Understanding
Spoken Language Understanding (SLU) is a key component of goal oriented
dialogue systems that would parse user utterances into semantic frame
representations. Traditionally SLU does not utilize the dialogue history beyond
the previous system turn and contextual ambiguities are resolved by the
downstream components. In this paper, we explore novel approaches for modeling
dialogue context in a recurrent neural network (RNN) based language
understanding system. We propose the Sequential Dialogue Encoder Network, that
allows encoding context from the dialogue history in chronological order. We
compare the performance of our proposed architecture with two context models,
one that uses just the previous turn context and another that encodes dialogue
context in a memory network, but loses the order of utterances in the dialogue
history. Experiments with a multi-domain dialogue dataset demonstrate that the
proposed architecture results in reduced semantic frame error rates.Comment: 8 + 2 pages, Updated 10/17: Updated typos in abstract, Updated 07/07:
Updated Title, abstract and few minor change
Neural Responding Machine for Short-Text Conversation
We propose Neural Responding Machine (NRM), a neural network-based response
generator for Short-Text Conversation. NRM takes the general encoder-decoder
framework: it formalizes the generation of response as a decoding process based
on the latent representation of the input text, while both encoding and
decoding are realized with recurrent neural networks (RNN). The NRM is trained
with a large amount of one-round conversation data collected from a
microblogging service. Empirical study shows that NRM can generate
grammatically correct and content-wise appropriate responses to over 75% of the
input text, outperforming state-of-the-arts in the same setting, including
retrieval-based and SMT-based models.Comment: accepted as a full paper at ACL 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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