Preserving Distributional Information in Dialogue Act Classification

Tran, Quan Hung; Zukerman, Ingrid; Haffari, Gholamreza

doi:10.18653/v1/d17-1229

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…• UCI [44] method embeds contextual information of utterance via hierarchical CNN/RNN for DA classification. • PDI [45] method predicts the next label based on the current label probability distribution to avoid label bias. • DRLM-Conditional [46] method is a latent variable recurrent neural network architecture for jointly modeling utterances and DA labels.…”

Section: Results and Analysismentioning

confidence: 99%

“…• DMN [23] methods is a DMN based method for sentiment classification, where BiLSTM is used for sentences embedding. • ADC [47] and PDI [45] methds are acoustic and discourse classification based on HMM and SVM. • GAN [48] method is a generative neural network which incorporates attention technique and a label-to-label connection.…”

Section: Results and Analysismentioning

confidence: 99%

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Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training

Wan

Yan

Gao

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Dialogue Act (DA) classification is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Currently, many existing approaches formulate the DA classification problem ranging from multi-classification to structured prediction, which suffer from two limitations: a) these methods are either handcrafted feature-based or have limited memories. b) adversarial examples can't be correctly classified by traditional training methods. To address these issues, in this paper we first cast the problem into a question and answering problem and proposed an improved dynamic memory networks with hierarchical pyramidal utterance encoder. Moreover, we apply adversarial training to train our proposed model. We evaluate our model on two public datasets, i.e., Switchboard dialogue act corpus and the MapTask corpus. Extensive experiments show that our proposed model is not only robust, but also achieves better performance when compared with some state-of-the-art baselines.

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Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training

Wan

Yan

Gao

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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“…Tran et al, 2017b;Chen et al, 2018;Li et al, 2019;Raheja & Tetreault, 2019). Tran et al (2017c) also observed that propagating uncertainty information concerning the previous predictions can lead to the prediction of better dialog act sequences. Finally, most of the studies that approach the task as a sequence labeling problem also rely on Conditional Random Fields (CRFs) (e.g.…”

Section: Automatic Dialog Act Recognitionmentioning

confidence: 93%

Automatic Recognition of the General-Purpose Communicative Functions Defined by the ISO 24617-2 Standard for Dialog Act Annotation

Ribeiro

Matos

2022

jair

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From the perspective of a dialog system, it is important to identify the intention behind the segments in a dialog, since it provides an important cue regarding the information that is present in the segments and how they should be interpreted. ISO 24617-2, the standard for dialog act annotation, defines a hierarchically organized set of general-purpose communicative functions which correspond to different intentions that are relevant in the context of a dialog. We explore the automatic recognition of these communicative functions in the DialogBank, which is a reference set of dialogs annotated according to this standard. To do so, we propose adaptations of existing approaches to flat dialog act recognition that allow them to deal with the hierarchical classification problem. More specifically, we propose the use of an end-to-end hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Furthermore, since the amount of dialogs in the DialogBank is small, we rely on transfer learning processes to reduce overfitting and improve performance. The results of our experiments show that our approach outperforms both a flat one and hierarchical approaches based on multiple classifiers and that each of its components plays an important role towards the recognition of general-purpose communicative functions.

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“…Usando esta abordagem eles alcançaram uma taxa de acerto de 74,5% no corpus SwDA e 63,3% no corpus HCRC Map Task Corpus (MapTask). Mais tarde, o desempenho no corpus SwDA foi melhorado para 75,6% usando um método baseado na propagação de informação de incerteza sobre as previsões anteriores (Tran et al, 2017c). Para além disso, utilizando mecanismos de atenção aplicadosàs células das camadas recorrentes no contexto de um modelo generativo, alcançaram uma taxa de acerto de 74,2% no corpus SwDA e 65,94% no corpus MapTask (Tran et al, 2017a).…”

Section: Estado Da Arte Em Reconhecimento De Actos De Diálogounclassified

Reconhecimento de Actos de Diálogo Hierárquicos e Multi-Etiqueta em Dados em Espanhol

Ribeiro

Ribeiro²,

Matos³

2019

Linguamática

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Os actos de diálogo revelam a intenção por trás das palavras pronunciadas. Por isso, o seu reconhecimento automático é importante para um sistema de diálogo que tenta entender o seu interlocutor. O estudo apresentado neste artigo aborda essa tarefa no corpus DIHANA, cujo esquema de anotação de actos de diálogo em três níveis coloca problemas que não foram explorados em estudos recentes. Além do problema hierárquico, os dois níveis inferiores colocam problemas de classificação multi-etiqueta. Além disso, cada nível da hierarquia refere-se a um aspecto diferente relativo à intenção do orador, tanto em termos da estrutura do diálogo, como da tarefa. Por outro lado, uma vez que os diálogos são em espanhol, este corpus permite-nos avaliar se as melhores abordagens para dados em inglês generalizam para uma língua diferente. Mais especificamente, comparamos o desempenho de diferentes abordagens de representação de segmentos, com foco tanto em sequências como em padrões de palavras, e avaliamos a importância do histórico do diálogo e das relações entre os múltiplos níveis da hierarquia. No que diz respeito ao problema de classificação de etiqueta única colocado pelo nível superior, mostramos que as conclusões obtidas a partir de dados em inglês se mantêm em dados em espanhol. Para além disso, mostramos que as abordagens podem ser adaptadas para cenários multi-etiqueta. Por fim, combinando hierarquicamente os melhores classificadores para cada nível, obtemos os melhores resultados reportados para este corpus.

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Preserving Distributional Information in Dialogue Act Classification

Cited by 11 publications

References 9 publications

Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training

Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training

Automatic Recognition of the General-Purpose Communicative Functions Defined by the ISO 24617-2 Standard for Dialog Act Annotation

Reconhecimento de Actos de Diálogo Hierárquicos e Multi-Etiqueta em Dados em Espanhol

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