303,943 research outputs found
Improving Background Based Conversation with Context-aware Knowledge Pre-selection
Background Based Conversations (BBCs) have been developed to make dialogue
systems generate more informative and natural responses by leveraging
background knowledge. Existing methods for BBCs can be grouped into two
categories: extraction-based methods and generation-based methods. The former
extract spans frombackground material as responses that are not necessarily
natural. The latter generate responses thatare natural but not necessarily
effective in leveraging background knowledge. In this paper, we focus on
generation-based methods and propose a model, namely Context-aware Knowledge
Pre-selection (CaKe), which introduces a pre-selection process that uses
dynamic bi-directional attention to improve knowledge selection by using the
utterance history context as prior information to select the most relevant
background material. Experimental results show that our model is superior to
current state-of-the-art baselines, indicating that it benefits from the
pre-selection process, thus improving in-formativeness and fluency.Comment: SCAI 2019 workshop pape
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
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