scholarly journals Dialogue state tracking accuracy improvement by distinguishing slot-value pairs and dialogue behaviour

2021 ◽  
Vol 21 (2) ◽  
pp. 1057
Author(s):  
Khaldoon H. Alhussayni ◽  
Alexander Zamyatin ◽  
S. Eman Alshamery
Keyword(s):  
Critical Role ◽  
Historical Context ◽  
Dialogue Systems ◽  
Tracking Accuracy ◽  
Dialogue System ◽  
Dialogue Acts ◽  
Proposed Model ◽  
State Tracking ◽  
Task Oriented ◽  
Previous User

<div><p>Dialog state tracking (DST) plays a critical role in cycle life of a task-oriented dialogue system. DST represents the goals of the consumer at each step by dialogue and describes such objectives as a conceptual structure comprising slot-value pairs and dialogue actions that specifically improve the performance and effectiveness of dialogue systems. DST faces several challenges: diversity of linguistics, dynamic social context and the dissemination of the state of dialogue over candidate values both in slot values and in dialogue acts determined in ontology. In many turns during the dialogue, users indirectly refer to the previous utterances, and that produce a challenge to distinguishing and use of related dialogue history, Recent methods used and popular for that are ineffective. In this paper, we propose a dialogue historical context self-Attention framework for DST that recognizes relevant historical context by including previous user utterance beside current user utterances and previous system actions where specific slot-value piers variations and uses that together with weighted system utterance to outperform existing models by recognizing the related context and the relevance of a system utterance. For the evaluation of the proposed model the WoZ dataset was used. The implementation was attempted with the prior user utterance as a dialogue encoder and second by the additional score combined with all the candidate slot-value pairs in the context of previous user utterances and current utterances. The proposed model obtained 0.8 per cent better results than all state-of-the-art methods in the combined precision of the target, but this is not the turnaround challenge for the submission.</p></div>

scholarly journals CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset

2020 ◽  
Vol 8 ◽  
pp. 281-295
Author(s):  
Qi Zhu ◽  
Kaili Huang ◽  
Zheng Zhang ◽  
Xiaoyan Zhu ◽  
Minlie Huang
Keyword(s):  
Large Scale ◽  
Dialogue Systems ◽  
Wizard Of Oz ◽  
Cross Domain ◽  
Large Size ◽  
User Simulation ◽  
Dialogue Acts ◽  
Dialogue Modeling ◽  
State Tracking ◽  
Task Oriented

To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts on both user and system sides. About 60% of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.

scholarly journals CERG: Chinese Emotional Response Generator with Retrieval Method

Research ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yangyang Zhou ◽  
Fuji Ren
Keyword(s):  
Emotional Response ◽  
Dialogue Systems ◽  
Inference Process ◽  
Dialogue System ◽  
Data Set ◽  
Retrieval Method ◽  
Input Text ◽  
Proposed Model ◽  
Semantic Relevance ◽  
Task Oriented

The dialogue system has always been one of the important topics in the domain of artificial intelligence. So far, most of the mature dialogue systems are task-oriented based, while non-task-oriented dialogue systems still have a lot of room for improvement. We propose a data-driven non-task-oriented dialogue generator “CERG” based on neural networks. This model has the emotion recognition capability and can generate corresponding responses. The data set we adopt comes from the NTCIR-14 STC-3 CECG subtask, which contains more than 1.7 million Chinese Weibo post-response pairs and 6 emotion categories. We try to concatenate the post and the response with the emotion, then mask the response part of the input text character by character to emulate the encoder-decoder framework. We use the improved transformer blocks as the core to build the model and add regularization methods to alleviate the problems of overcorrection and exposure bias. We introduce the retrieval method to the inference process to improve the semantic relevance of generated responses. The results of the manual evaluation show that our proposed model can make different responses to different emotions to improve the human-computer interaction experience. This model can be applied to lots of domains, such as automatic reply robots of social application.

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.

Pragmatics and Dialogue

2012 ◽  
Author(s):  
Geoffrey Leech
Keyword(s):  
Speech Acts ◽  
Dialogue Systems ◽  
Human Beings ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Cooperative Principle ◽  
Dialogue Acts ◽  
Analysis And Synthesis ◽  
History Of ◽  
Task Oriented

This article introduces the linguistic subdiscipline of pragmatics and shows how this is being applied to the development of spoken dialogue systems — currently perhaps the most important applications area for computational pragmatics. It traces the history of pragmatics from its philosophical roots, and outlines some key notions of theoretical pragmatics — speech acts, illocutionary force, the cooperative principle and relevance. It then discusses the application of pragmatics to dialogue modelling, especially the development of spoken dialogue systems intended to interact with human beings in task-oriented scenarios such as providing travel information and shows how and why computational pragmatics differs from ‘linguistic’ pragmatics, and how pragmatics contributes to the computational analysis of dialogues. One major illustration of this is the application of speech act theory in the analysis and synthesis of service interactions in terms of dialogue acts.

scholarly journals A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
A-Yeong Kim ◽  
Hyun-Je Song ◽  
Seong-Bae Park
Keyword(s):  
Attention Mechanism ◽  
Data Set ◽  
Dialog Systems ◽  
Dialog System ◽  
Fast Training ◽  
Proposed Model ◽  
Spoken Dialog System ◽  
State Tracking ◽  
Dialog State Tracking ◽  
Task Oriented

Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance. Since the success of a dialog is influenced by the ability of the system to catch the requirements of the user, accurate state tracking is important for spoken dialog systems. This paper proposes a two-step neural dialog state tracker which is composed of an informativeness classifier and a neural tracker. The informativeness classifier which is implemented by a CNN first filters out noninformative utterances in a dialog. Then, the neural tracker estimates dialog states from the remaining informative utterances. The tracker adopts the attention mechanism and the hierarchical softmax for its performance and fast training. To prove the effectiveness of the proposed model, we do experiments on dialog state tracking in the human-human task-oriented dialogs with the standard DSTC4 data set. Our experimental results prove the effectiveness of the proposed model by showing that the proposed model outperforms the neural trackers without the informativeness classifier, the attention mechanism, or the hierarchical softmax.

Aspect-Aware Response Generation for Multimodal Dialogue System

2021 ◽  
Vol 12 (2) ◽  
pp. 1-33
Author(s):  
Mauajama Firdaus ◽  
Nidhi Thakur ◽  
Asif Ekbal
Keyword(s):  
User Satisfaction ◽  
Dialogue Systems ◽  
Conversational Agents ◽  
Dialogue System ◽  
Multimodal Systems ◽  
Dialog System ◽  
Language And Vision ◽  
Memory Network ◽  
Multimodal Dialogue ◽  
Task Oriented

Multimodality in dialogue systems has opened up new frontiers for the creation of robust conversational agents. Any multimodal system aims at bridging the gap between language and vision by leveraging diverse and often complementary information from image, audio, and video, as well as text. For every task-oriented dialog system, different aspects of the product or service are crucial for satisfying the user’s demands. Based upon the aspect, the user decides upon selecting the product or service. The ability to generate responses with the specified aspects in a goal-oriented dialogue setup facilitates user satisfaction by fulfilling the user’s goals. Therefore, in our current work, we propose the task of aspect controlled response generation in a multimodal task-oriented dialog system. We employ a multimodal hierarchical memory network for generating responses that utilize information from both text and images. As there was no readily available data for building such multimodal systems, we create a Multi-Domain Multi-Modal Dialog (MDMMD++) dataset. The dataset comprises the conversations having both text and images belonging to the four different domains, such as hotels, restaurants, electronics, and furniture. Quantitative and qualitative analysis on the newly created MDMMD++ dataset shows that the proposed methodology outperforms the baseline models for the proposed task of aspect controlled response generation.

Study on Development of Humor Discriminator for Dialogue System

2020 ◽  
Vol 24 (3) ◽  
pp. 422-435
Author(s):  
Tomohiro Yoshikawa ◽  
◽  
Ryosuke Iwakura
Keyword(s):  
Human Subjects ◽  
The Other ◽  
P Value ◽  
Dialogue Systems ◽  
Generation System ◽  
Dialogue System ◽  
Evaluation Scores ◽  
Significant Difference ◽  
Computer Based ◽  
Task Oriented

Studies on automatic dialogue systems, which allow people and computers to communicate with each other using natural language, have been attracting attention. In particular, the main objective of a non-task-oriented dialogue system is not to achieve a specific task but to amuse users through chat and free dialogue. For this type of dialogue system, continuity of the dialogue is important because users can easily get tired if the dialogue is monotonous. On the other hand, preceding studies have shown that speech with humorous expressions is effective in improving the continuity of a dialogue. In this study, we developed a computer-based humor discriminator to perform user- or situation-independent objective discrimination of humor. Using the humor discriminator, we also developed an automatic humor generation system and conducted an evaluation experiment with human subjects to test the generated jokes. A t-test on the evaluation scores revealed a significant difference (P value: 3.5×10-5) between the proposed and existing methods of joke generation.

scholarly journals Estimating the number of segments for improving dialogue act labelling

2011 ◽  
Vol 18 (1) ◽  
pp. 1-19 ◽  
Author(s):  
VICENT TAMARIT ◽  
CARLOS-D. MARTÍNEZ-HINAREJOS ◽  
JOSÉ-MIGUEL BENEDÍ
Keyword(s):  
Functional Role ◽  
Probabilistic Models ◽  
Dialogue Systems ◽  
Dialogue System ◽  
New Approach ◽  
Dialogue Acts ◽  
Dialogue Manager ◽  
Number Of Segments

AbstractIn dialogue systems it is important to label the dialogue turns with dialogue-related meaning. Each turn is usually divided into segments and these segments are labelled with dialogue acts (DAs). A DA is a representation of the functional role of the segment. Each segment is labelled with one DA, representing its role in the ongoing discourse. The sequence of DAs given a dialogue turn is used by the dialogue manager to understand the turn. Probabilistic models that perform DA labelling can be used on segmented or unsegmented turns. The last option is more likely for a practical dialogue system, but it provides poorer results. In that case, a hypothesis for the number of segments can be provided to improve the results. We propose some methods to estimate the probability of the number of segments based on the transcription of the turn. The new labelling model includes the estimation of the probability of the number of segments in the turn. We tested this new approach with two different dialogue corpora: SwitchBoard and Dihana. The results show that this inclusion significantly improves the labelling accuracy.

scholarly journals TODO: A Core Ontology for Task-Oriented Dialogue Systems in Industry 4.0

2021 ◽  
Author(s):  
Cristina Aceta ◽  
Izaskun Fernández ◽  
Aitor Soroa
Keyword(s):  
Industry 4.0 ◽  
Use Cases ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Industrial Systems ◽  
Dialogue Management ◽  
Core Ontology ◽  
Dialogue Modelling ◽  
The One ◽  
Task Oriented

Nowadays, the demand in industry of dialogue systems to be able to naturally communicate with industrial systems is increasing, as they allow to enhance productivity and security in these scenarios. However, adapting these systems to different use cases is a costly process, due to the complexity of the scenarios and the lack of available data. This work presents the Task-Oriented Dialogue management Ontology (TODO), which aims to provide a core and complete base for semantic-based task-oriented dialogue systems in the context of industrial scenarios in terms of, on the one hand, domain and dialogue modelling and, on the other hand, dialogue management and tracing support. Furthermore, its modular structure, besides grouping specific knowledge in independent components, allows to easily extend each of the modules, attending the necessities of the different use cases. These characteristics allow an easy adaptation of the ontology to different use cases, with a considerable reduction of time and costs. So as to demonstrate the capabilities of the the ontology by integrating it in a task-oriented dialogue system, TODO has been validated in real-world use cases. Finally, an evaluation is also presented, covering different relevant aspects of the ontology.

scholarly journals More to diverse: Generating diversified responses in a task oriented multimodal dialog system

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241271
Author(s):  
Mauajama Firdaus ◽  
Arunav Pratap Shandeelya ◽  
Asif Ekbal
Keyword(s):  
Large Scale ◽  
Contextual Information ◽  
Empirical Evaluation ◽  
Dialogue Systems ◽  
Beam Search ◽  
Dialogue System ◽  
Text And Image ◽  
Dialog System ◽  
Multimodal Dialogue ◽  
Task Oriented

Multimodal dialogue system, due to its many-fold applications, has gained much attention to the researchers and developers in recent times. With the release of large-scale multimodal dialog dataset Saha et al. 2018 on the fashion domain, it has been possible to investigate the dialogue systems having both textual and visual modalities. Response generation is an essential aspect of every dialogue system, and making the responses diverse is an important problem. For any goal-oriented conversational agent, the system’s responses must be informative, diverse and polite, that may lead to better user experiences. In this paper, we propose an end-to-end neural framework for generating varied responses in a multimodal dialogue setup capturing information from both the text and image. Multimodal encoder with co-attention between the text and image is used for focusing on the different modalities to obtain better contextual information. For effective information sharing across the modalities, we combine the information of text and images using the BLOCK fusion technique that helps in learning an improved multimodal representation. We employ stochastic beam search with Gumble Top K-tricks to achieve diversified responses while preserving the content and politeness in the responses. Experimental results show that our proposed approach performs significantly better compared to the existing and baseline methods in terms of distinct metrics, and thereby generates more diverse responses that are informative, interesting and polite without any loss of information. Empirical evaluation also reveals that images, while used along with the text, improve the efficiency of the model in generating diversified responses.

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