The method of building expectation model in task-oriented dialogue systems and its realization algorithms

In the framework of man-machine communication, oral dialogue has a particular place since human speech presents several advantages when used either alone or in multimedia interfaces. The last decade has witnessed a proliferation of research into speech recognition and understanding, but few systems have been defined with a view to managing and understanding an actual man-machine dialogue. The PARTNER system that we describe in this paper proposes a solution in the case of task oriented dialogue with the use of artificial languages. A description of the essential characteristics of dialogue systems is followed by a presentation of the architecture and the principles of the PARTNER system. Finally, we present the most recent results obtained in the oral management of electronic mail in French and German.

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End-to-end optimization of goal-driven and visually grounded dialogue systems

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/385 ◽

2017 ◽

Cited By ~ 16

Author(s):

Florian Strub ◽

Harm de Vries ◽

Jérémie Mary ◽

Bilal Piot ◽

Aaron Courville ◽

...

Keyword(s):

Gradient Algorithm ◽

Planning Problem ◽

Dialogue Systems ◽

Generation Task ◽

Policy Gradient ◽

Specific Object ◽

Complex Picture ◽

History Of ◽

End To End ◽

Task Oriented

End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as a supervised learning problem, aiming at predicting the next utterance of a participant given the full history of the dialogue. This vision may fail to correctly render the planning problem inherent to dialogue as well as its contextual and grounded nature. In this paper, we introduce a Deep Reinforcement Learning method to optimize visually grounded task-oriented dialogues, based on the policy gradient algorithm. This approach is tested on the question generation task from the dataset GuessWhat?! containing 120k dialogues and provides encouraging results at solving both the problem of generating natural dialogues and the task of discovering a specific object in a complex picture.

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An Evaluation of Chinese Human-Computer Dialogue Technology

Data Intelligence ◽

10.1162/dint_a_00007 ◽

2019 ◽

Vol 1 (2) ◽

pp. 187-200

Author(s):

Zhengyu Zhao ◽

Weinan Zhang ◽

Wanxiang Che ◽

Zhigang Chen ◽

Yibo Zhang

Keyword(s):

Artificial Intelligence ◽

Large Scale ◽

Data Sets ◽

Dialogue Systems ◽

Online Testing ◽

User Intent ◽

Existing Problems ◽

Intelligent Processing ◽

Task Oriented ◽

Important Branch

The human-computer dialogue has recently attracted extensive attention from both academia and industry as an important branch in the field of artificial intelligence (AI). However, there are few studies on the evaluation of large-scale Chinese human-computer dialogue systems. In this paper, we introduce the Second Evaluation of Chinese Human-Computer Dialogue Technology, which focuses on the identification of a user's intents and intelligent processing of intent words. The Evaluation consists of user intent classification (Task 1) and online testing of task-oriented dialogues (Task 2), the data sets of which are provided by iFLYTEK Corporation. The evaluation tasks and data sets are introduced in detail, and meanwhile, the evaluation results and the existing problems in the evaluation are discussed.

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Memory-Augmented Dialogue Management for Task-Oriented Dialogue Systems

ACM Transactions on Information Systems ◽

10.1145/3317612 ◽

2019 ◽

Vol 37 (3) ◽

pp. 1-30 ◽

Cited By ~ 2

Author(s):

Zheng Zhang ◽

Minlie Huang ◽

Zhongzhou Zhao ◽

Feng Ji ◽

Haiqing Chen ◽

...

Keyword(s):

Dialogue Systems ◽

Dialogue Management ◽

Task Oriented

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Affirmative Cue Words in Task-Oriented Dialogue

Computational Linguistics ◽

10.1162/coli_a_00083 ◽

2012 ◽

Vol 38 (1) ◽

pp. 1-39 ◽

Cited By ~ 18

Author(s):

Agustín Gravano ◽

Julia Hirschberg ◽

Štefan Beňuš

Keyword(s):

Computational Methods ◽

Predictive Power ◽

American English ◽

Contextual Information ◽

Spoken Language ◽

Dialogue Systems ◽

Spoken Dialogue Systems ◽

Standard American English ◽

Task Oriented ◽

Cue Words

We present a series of studies of affirmative cue words—a family of cue words such as “okay” or “alright” that speakers use frequently in conversation. These words pose a challenge for spoken dialogue systems because of their ambiguity: They may be used for agreeing with what the interlocutor has said, indicating continued attention, or for cueing the start of a new topic, among other meanings. We describe differences in the acoustic/prosodic realization of such functions in a corpus of spontaneous, task-oriented dialogues in Standard American English. These results are important both for interpretation and for production in spoken language applications. We also assess the predictive power of computational methods for the automatic disambiguation of these words. We find that contextual information and final intonation figure as the most salient cues to automatic disambiguation.

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