18,275 research outputs found
Spoken Language Intent Detection using Confusion2Vec
Decoding speaker's intent is a crucial part of spoken language understanding
(SLU). The presence of noise or errors in the text transcriptions, in real life
scenarios make the task more challenging. In this paper, we address the spoken
language intent detection under noisy conditions imposed by automatic speech
recognition (ASR) systems. We propose to employ confusion2vec word feature
representation to compensate for the errors made by ASR and to increase the
robustness of the SLU system. The confusion2vec, motivated from human speech
production and perception, models acoustic relationships between words in
addition to the semantic and syntactic relations of words in human language. We
hypothesize that ASR often makes errors relating to acoustically similar words,
and the confusion2vec with inherent model of acoustic relationships between
words is able to compensate for the errors. We demonstrate through experiments
on the ATIS benchmark dataset, the robustness of the proposed model to achieve
state-of-the-art results under noisy ASR conditions. Our system reduces
classification error rate (CER) by 20.84% and improves robustness by 37.48%
(lower CER degradation) relative to the previous state-of-the-art going from
clean to noisy transcripts. Improvements are also demonstrated when training
the intent detection models on noisy transcripts
Label-Dependencies Aware Recurrent Neural Networks
In the last few years, Recurrent Neural Networks (RNNs) have proved effective
on several NLP tasks. Despite such great success, their ability to model
\emph{sequence labeling} is still limited. This lead research toward solutions
where RNNs are combined with models which already proved effective in this
domain, such as CRFs. In this work we propose a solution far simpler but very
effective: an evolution of the simple Jordan RNN, where labels are re-injected
as input into the network, and converted into embeddings, in the same way as
words. We compare this RNN variant to all the other RNN models, Elman and
Jordan RNN, LSTM and GRU, on two well-known tasks of Spoken Language
Understanding (SLU). Thanks to label embeddings and their combination at the
hidden layer, the proposed variant, which uses more parameters than Elman and
Jordan RNNs, but far fewer than LSTM and GRU, is more effective than other
RNNs, but also outperforms sophisticated CRF models.Comment: 22 pages, 3 figures. Accepted at CICling 2017 conference. Best
Verifiability, Reproducibility, and Working Description awar
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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