23,986 research outputs found
Dialogue Act Recognition via CRF-Attentive Structured Network
Dialogue Act Recognition (DAR) is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DAR problem ranging from multi-classification to structured
prediction, which suffer from handcrafted feature extensions and attentive
contextual structural dependencies. In this paper, we consider the problem of
DAR from the viewpoint of extending richer Conditional Random Field (CRF)
structural dependencies without abandoning end-to-end training. We incorporate
hierarchical semantic inference with memory mechanism on the utterance
modeling. We then extend structured attention network to the linear-chain
conditional random field layer which takes into account both contextual
utterances and corresponding dialogue acts. The extensive experiments on two
major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder
Dialogue Act (MRDA) datasets show that our method achieves better performance
than other state-of-the-art solutions to the problem. It is a remarkable fact
that our method is nearly close to the human annotator's performance on SWDA
within 2% gap.Comment: 10 pages, 4figure
A Differentiable Generative Adversarial Network for Open Domain Dialogue
Paper presented at the IWSDS 2019: International Workshop on Spoken Dialogue Systems Technology, Siracusa, Italy, April 24-26, 2019This work presents a novel methodology to train open domain neural dialogue systems within the framework of Generative Adversarial Networks with gradient-based optimization methods. We avoid the non-differentiability related to text-generating networks approximating the word vector corresponding to each generated token via a top-k softmax. We show that a weighted average of the word vectors of the most probable tokens computed from the probabilities resulting of the top-k softmax leads to a good approximation of the word vector of the generated token. Finally we demonstrate through a human evaluation process that training a neural dialogue system via adversarial learning with this method successfully discourages it from producing generic responses. Instead it tends to produce more informative and variate ones.This work has been partially funded by the Basque Government under grant PRE_2017_1_0357, by the University of the Basque Country UPV/EHU under grant PIF17/310, and by the H2020 RIA EMPATHIC (Grant N: 769872)
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