788 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
Answer Acquisition for Knowledge Base Question Answering Systems Based on Dynamic Memory Network
In recent years, with the rapid growth of Artificial Intelligence (AI) and the Internet of Things (IoT), the question answering systems for human-machine interaction based on deep learning have become a research hotspot of the IoT. Different from the structured query method in traditional Knowledge Base Question Answering (KBQA) systems based on templates or rules, representation learning is one of the most promising approaches to solving the problems of data sparsity and semantic gaps. In this paper, an answer acquisition method for KBQA systems based on a dynamic memory network is proposed, in which representation learning is employed to represent the natural language questions that are raised by users and the knowledge base subgraphs of the related entities. These representations are taken as inputs of the dynamic memory network. The correct answers are obtained by utilizing the memory and inferential capabilities. The experimental results demonstrate the effectiveness of the proposed approach. - 2013 IEEE.This work was supported by the National Science Foundation of China under Grant 61365010.Scopu
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