Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields

Valentin Barriere; Chloe Clavel; Slim Essid

doi:10.1109/ICASSP.2018.8462160

Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields

Valentin Barriere
, Chloe Clavel
, Slim Essid

Université Paris-Saclay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, the main goal is to classify, in a human-agent interaction, the attitude of the user using hidden conditional random fields. This model allows us to capture the dynamics of the interaction in the pairs of speech turns (adjacency pairs) analyzed by our system. High level linguistic features are computed at word level. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the SEMAINE corpus. We obtain a Fl-score of 0.80, labeling using the most probable sequence of hidden states.

Original language	English
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4949-4953
Number of pages	5
ISBN (Print)	9781538646588
DOIs	https://doi.org/10.1109/ICASSP.2018.8462160
Publication status	Published - 10 Sept 2018
Externally published	Yes
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2018-April
ISSN (Print)	1520-6149

Conference

Conference	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	15/04/18 → 20/04/18

Keywords

Attitude Detection
Hidden Conditional Random Field
Linguistic Patterns
Opinion Mining

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10.1109/ICASSP.2018.8462160

Cite this

Barriere, V., Clavel, C., & Essid, S. (2018). Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 4949-4953). Article 8462160 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462160

Barriere, Valentin ; Clavel, Chloe ; Essid, Slim. / Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 4949-4953 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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abstract = "In this paper, the main goal is to classify, in a human-agent interaction, the attitude of the user using hidden conditional random fields. This model allows us to capture the dynamics of the interaction in the pairs of speech turns (adjacency pairs) analyzed by our system. High level linguistic features are computed at word level. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the SEMAINE corpus. We obtain a Fl-score of 0.80, labeling using the most probable sequence of hidden states.",

keywords = "Attitude Detection, Hidden Conditional Random Field, Linguistic Patterns, Opinion Mining",

author = "Valentin Barriere and Chloe Clavel and Slim Essid",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",

year = "2018",

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series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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Barriere, V, Clavel, C & Essid, S 2018, Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings., 8462160, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2018-April, Institute of Electrical and Electronics Engineers Inc., pp. 4949-4953, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 15/04/18. https://doi.org/10.1109/ICASSP.2018.8462160

Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields. / Barriere, Valentin; Clavel, Chloe; Essid, Slim.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 4949-4953 8462160 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Clavel, Chloe

AU - Essid, Slim

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Y1 - 2018/9/10

N2 - In this paper, the main goal is to classify, in a human-agent interaction, the attitude of the user using hidden conditional random fields. This model allows us to capture the dynamics of the interaction in the pairs of speech turns (adjacency pairs) analyzed by our system. High level linguistic features are computed at word level. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the SEMAINE corpus. We obtain a Fl-score of 0.80, labeling using the most probable sequence of hidden states.

AB - In this paper, the main goal is to classify, in a human-agent interaction, the attitude of the user using hidden conditional random fields. This model allows us to capture the dynamics of the interaction in the pairs of speech turns (adjacency pairs) analyzed by our system. High level linguistic features are computed at word level. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the SEMAINE corpus. We obtain a Fl-score of 0.80, labeling using the most probable sequence of hidden states.

KW - Attitude Detection

KW - Hidden Conditional Random Field

KW - Linguistic Patterns

KW - Opinion Mining

UR - https://www.scopus.com/pages/publications/85054218754

U2 - 10.1109/ICASSP.2018.8462160

DO - 10.1109/ICASSP.2018.8462160

M3 - Conference contribution

AN - SCOPUS:85054218754

SN - 9781538646588

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 4949

EP - 4953

BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

Y2 - 15 April 2018 through 20 April 2018

ER -

Barriere V, Clavel C, Essid S. Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 4949-4953. 8462160. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2018.8462160

Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields

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