4,096 research outputs found
Deep Contextual Language Understanding in Spoken Dialogue Systems
Abstract We describe a unified multi-turn multi-task spoken language understanding (SLU) solution capable of handling multiple context sensitive classification (intent determination) and sequence labeling (slot filling) tasks simultaneously. The proposed architecture is based on recurrent convolutional neural networks (RCNN) with shared feature layers and globally normalized sequence modeling components. The temporal dependencies within and across different tasks are encoded succinctly as recurrent connections. The dialog system responses beyond SLU component are also exploited as effective external features. We show with extensive experiments on a number of datasets that the proposed joint learning framework generates state-of-the-art results for both classification and tagging, and the contextual modeling based on recurrent and external features significantly improves the context sensitivity of SLU models
Image-based Text Classification using 2D Convolutional Neural Networks
We propose a new approach to text classification
in which we consider the input text as an image and apply
2D Convolutional Neural Networks to learn the local and
global semantics of the sentences from the variations of the
visual patterns of words. Our approach demonstrates that
it is possible to get semantically meaningful features from
images with text without using optical character recognition
and sequential processing pipelines, techniques that traditional
natural language processing algorithms require. To validate
our approach, we present results for two applications: text
classification and dialog modeling. Using a 2D Convolutional
Neural Network, we were able to outperform the state-ofart
accuracy results for a Chinese text classification task and
achieved promising results for seven English text classification
tasks. Furthermore, our approach outperformed the memory
networks without match types when using out of vocabulary
entities from Task 4 of the bAbI dialog dataset
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
We present a novel response generation system that can be trained end to end
on large quantities of unstructured Twitter conversations. A neural network
architecture is used to address sparsity issues that arise when integrating
contextual information into classic statistical models, allowing the system to
take into account previous dialog utterances. Our dynamic-context generative
models show consistent gains over both context-sensitive and
non-context-sensitive Machine Translation and Information Retrieval baselines.Comment: A. Sordoni, M. Galley, M. Auli, C. Brockett, Y. Ji, M. Mitchell,
J.-Y. Nie, J. Gao, B. Dolan. 2015. A Neural Network Approach to
Context-Sensitive Generation of Conversational Responses. In Proc. of
NAACL-HLT. Pages 196-20
The Microsoft 2017 Conversational Speech Recognition System
We describe the 2017 version of Microsoft's conversational speech recognition
system, in which we update our 2016 system with recent developments in
neural-network-based acoustic and language modeling to further advance the
state of the art on the Switchboard speech recognition task. The system adds a
CNN-BLSTM acoustic model to the set of model architectures we combined
previously, and includes character-based and dialog session aware LSTM language
models in rescoring. For system combination we adopt a two-stage approach,
whereby subsets of acoustic models are first combined at the senone/frame
level, followed by a word-level voting via confusion networks. We also added a
confusion network rescoring step after system combination. The resulting system
yields a 5.1\% word error rate on the 2000 Switchboard evaluation set
Effective Spoken Language Labeling with Deep Recurrent Neural Networks
Understanding spoken language is a highly complex problem, which can be
decomposed into several simpler tasks. In this paper, we focus on Spoken
Language Understanding (SLU), the module of spoken dialog systems responsible
for extracting a semantic interpretation from the user utterance. The task is
treated as a labeling problem. In the past, SLU has been performed with a wide
variety of probabilistic models. The rise of neural networks, in the last
couple of years, has opened new interesting research directions in this domain.
Recurrent Neural Networks (RNNs) in particular are able not only to represent
several pieces of information as embeddings but also, thanks to their recurrent
architecture, to encode as embeddings relatively long contexts. Such long
contexts are in general out of reach for models previously used for SLU. In
this paper we propose novel RNNs architectures for SLU which outperform
previous ones. Starting from a published idea as base block, we design new deep
RNNs achieving state-of-the-art results on two widely used corpora for SLU:
ATIS (Air Traveling Information System), in English, and MEDIA (Hotel
information and reservation in France), in French.Comment: 8 pages. Rejected from IJCAI 2017, good remarks overall, but slightly
off-topic as from global meta-reviews. Recommendations: 8, 6, 6, 4. arXiv
admin note: text overlap with arXiv:1706.0174
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