Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields
| Autor: | Slim Essid, Chloé Clavel, Valentin Barriere |
|---|---|
| Přispěvatelé: | Département Images, Données, Signal (IDS), Télécom ParisTech, Signal, Statistique et Apprentissage (S2A), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Institut Mines-Télécom [Paris] (IMT)-Télécom Paris |
| Rok vydání: | 2018 |
| Předmět: |
Conditional random field
Sequence Context model Word embedding Computer science business.industry Adjacency pairs Pattern recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Syntax Data modeling [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Artificial intelligence business ComputingMilieux_MISCELLANEOUS Word (computer architecture) 0105 earth and related environmental sciences |
| Zdroj: | ICASSP ICASSP 2018-2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ICASSP 2018-2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2018, Calgary, Canada. pp.4949-4953, ⟨10.1109/ICASSP.2018.8462160⟩ |
| DOI: | 10.1109/icassp.2018.8462160 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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