10,197 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
Unsupervised Dialogue Act Induction using Gaussian Mixtures
This paper introduces a new unsupervised approach for dialogue act induction.
Given the sequence of dialogue utterances, the task is to assign them the
labels representing their function in the dialogue.
Utterances are represented as real-valued vectors encoding their meaning. We
model the dialogue as Hidden Markov model with emission probabilities estimated
by Gaussian mixtures. We use Gibbs sampling for posterior inference.
We present the results on the standard Switchboard-DAMSL corpus. Our
algorithm achieves promising results compared with strong supervised baselines
and outperforms other unsupervised algorithms.Comment: Accepted to EACL 201
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
We describe a statistical approach for modeling dialogue acts in
conversational speech, i.e., speech-act-like units such as Statement, Question,
Backchannel, Agreement, Disagreement, and Apology. Our model detects and
predicts dialogue acts based on lexical, collocational, and prosodic cues, as
well as on the discourse coherence of the dialogue act sequence. The dialogue
model is based on treating the discourse structure of a conversation as a
hidden Markov model and the individual dialogue acts as observations emanating
from the model states. Constraints on the likely sequence of dialogue acts are
modeled via a dialogue act n-gram. The statistical dialogue grammar is combined
with word n-grams, decision trees, and neural networks modeling the
idiosyncratic lexical and prosodic manifestations of each dialogue act. We
develop a probabilistic integration of speech recognition with dialogue
modeling, to improve both speech recognition and dialogue act classification
accuracy. Models are trained and evaluated using a large hand-labeled database
of 1,155 conversations from the Switchboard corpus of spontaneous
human-to-human telephone speech. We achieved good dialogue act labeling
accuracy (65% based on errorful, automatically recognized words and prosody,
and 71% based on word transcripts, compared to a chance baseline accuracy of
35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling
changed
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