@inproceedings{rojas-barahona-etal-2016-exploiting,
title = "Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding",
author = "Rojas-Barahona, Lina M. and
Ga{\v{s}}i{\'c}, Milica and
Mrk{\v{s}}i{\'c}, Nikola and
Su, Pei-Hao and
Ultes, Stefan and
Wen, Tsung-Hsien and
Young, Steve",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1025",
pages = "258--267",
abstract = "This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing ). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).",
}
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<abstract>This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing ). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).</abstract>
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%0 Conference Proceedings
%T Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
%A Rojas-Barahona, Lina M.
%A Gašić, Milica
%A Mrkšić, Nikola
%A Su, Pei-Hao
%A Ultes, Stefan
%A Wen, Tsung-Hsien
%A Young, Steve
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F rojas-barahona-etal-2016-exploiting
%X This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing ). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).
%U https://aclanthology.org/C16-1025
%P 258-267
Markdown (Informal)
[Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding](https://aclanthology.org/C16-1025) (Rojas-Barahona et al., COLING 2016)
ACL
- Lina M. Rojas-Barahona, Milica Gašić, Nikola Mrkšić, Pei-Hao Su, Stefan Ultes, Tsung-Hsien Wen, and Steve Young. 2016. Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 258–267, Osaka, Japan. The COLING 2016 Organizing Committee.