scholarly journals Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems

2020 ◽  
Author(s):  
Andrea Madotto ◽  
Samuel Cahyawijaya ◽  
Genta Indra Winata ◽  
Yan Xu ◽  
Zihan Liu ◽  
...  
Keyword(s):  
Knowledge Bases ◽  
Dialogue Systems ◽  
Task Oriented

scholarly journals Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems

2021 ◽  
Author(s):  
Yanjie Gou ◽  
Yinjie Lei ◽  
Lingqiao Liu ◽  
Yong Dai ◽  
Chunxu Shen
Keyword(s):  
Knowledge Bases ◽  
Dialogue Systems ◽  
End To End ◽  
Task Oriented

scholarly journals UniMF: A Unified Framework to Incorporate Multimodal Knowledge Bases intoEnd-to-End Task-Oriented Dialogue Systems

2021 ◽  
Author(s):  
Shiquan Yang ◽  
Rui Zhang ◽  
Sarah M. Erfani ◽  
Jey Han Lau
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Language Models ◽  
Dialogue Systems ◽  
Unified Framework ◽  
Proposed Model ◽  
Multimodal Information ◽  
Task Oriented ◽  
Novel Model ◽  
Single Modality

Knowledge bases (KBs) are usually essential for building practical dialogue systems. Recently we have seen rapidly growing interest in integrating knowledge bases into dialogue systems. However, existing approaches mostly deal with knowledge bases of a single modality, typically textual information. As today's knowledge bases become abundant with multimodal information such as images, audios and videos, the limitation of existing approaches greatly hinders the development of dialogue systems. In this paper, we focus on task-oriented dialogue systems and address this limitation by proposing a novel model that integrates external multimodal KB reasoning with pre-trained language models. We further enhance the model via a novel multi-granularity fusion mechanism to capture multi-grained semantics in the dialogue history. To validate the effectiveness of the proposed model, we collect a new large-scale (14K) dialogue dataset MMDialKB, built upon multimodal KB. Both automatic and human evaluation results on MMDialKB demonstrate the superiority of our proposed framework over strong baselines.

A MULTILINGUAL APPROACH TO TASK-ORIENTED MAN-MACHINE DIALOGUE BY VOICE

1988 ◽  
Vol 02 (03) ◽  
pp. 573-588
Author(s):  
PHILIPPE MORIN ◽  
JEAN-PAUL HATON ◽  
JEAN-MARIE PIERREL ◽  
GUENTHER RUSKE ◽  
WALTER WEIGEL
Keyword(s):  
Speech Recognition ◽  
Electronic Mail ◽  
Dialogue Systems ◽  
Human Speech ◽  
Artificial Languages ◽  
Man Machine Dialogue ◽  
Machine Communication ◽  
Task Oriented ◽  
Multilingual Approach ◽  
Multimedia Interfaces

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.

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

2017 ◽  
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.

scholarly journals From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling

2019 ◽  
Vol 8 (9) ◽  
pp. 376 ◽  
Author(s):  
Zhi-Wei Hou ◽  
Cheng-Zhi Qin ◽  
A-Xing Zhu ◽  
Peng Liang ◽  
Yi-Jie Wang ◽  
...  
Keyword(s):  
Service Composition ◽  
Input Data ◽  
Knowledge Bases ◽  
Future Research ◽  
Knowledge Representation And Reasoning ◽  
Data Preparation ◽  
Preparation Methods ◽  
Construction Of Knowledge ◽  
Task Oriented ◽  
Geographic Modeling

One of the key concerns in geographic modeling is the preparation of input data that are sufficient and appropriate for models. This requires considerable time, effort, and expertise since geographic models and their application contexts are complex and diverse. Moreover, both data and data pre-processing tools are multi-source, heterogeneous, and sometimes unavailable for a specific application context. The traditional method of manually preparing input data cannot effectively support geographic modeling, especially for complex integrated models and non-expert users. Therefore, effective methods are urgently needed that are not only able to prepare appropriate input data for models but are also easy to use. In this review paper, we first analyze the factors that influence data preparation and discuss the three corresponding key tasks that should be accomplished when developing input data preparation methods for geographic models. Then, existing input data preparation methods for geographic models are discussed through classifying into three categories: manual, (semi-)automatic, and intelligent (i.e., not only (semi-)automatic but also adaptive to application context) methods. Supported by the adoption of knowledge representation and reasoning techniques, the state-of-the-art methods in this field point to intelligent input data preparation for geographic models, which includes knowledge-supported discovery and chaining of data pre-processing functionalities, knowledge-driven (semi-)automatic workflow building (or service composition in the context of geographic web services) of data preprocessing, and artificial intelligent planning-based service composition as well as their parameter-settings. Lastly, we discuss the challenges and future research directions from the following aspects: Sharing and reusing of model data and workflows, integration of data discovery and processing functionalities, task-oriented input data preparation methods, and construction of knowledge bases for geographic modeling, all assisting with the development of an easy-to-use geographic modeling environment with intelligent input data preparation.

scholarly journals An Evaluation of Chinese Human-Computer Dialogue Technology

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.

scholarly journals Memory-Augmented Dialogue Management for Task-Oriented Dialogue Systems

2019 ◽  
Vol 37 (3) ◽  
pp. 1-30 ◽  
Author(s):  
Zheng Zhang ◽  
Minlie Huang ◽  
Zhongzhou Zhao ◽  
Feng Ji ◽  
Haiqing Chen ◽  
...  
Keyword(s):  
Dialogue Systems ◽  
Dialogue Management ◽  
Task Oriented
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