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 An Evaluation of Chinese Human-Computer Dialogue Technology

2021 ◽  
pp. 1-14
Author(s):  
Zixian Feng ◽  
Caihai Zhu ◽  
Weinan Zhang ◽  
Zhigang Chen ◽  
Wanxiang Che ◽  
...  
Keyword(s):  
Large Scale ◽  
Spoken Language ◽  
Data Sets ◽  
Dialogue Systems ◽  
Language Understanding ◽  
Existing Problems ◽  
Spoken Language Understanding ◽  
Institute Of Technology ◽  
Important Branch ◽  
Evaluation Work

There is a growing interest in developing human-computer dialogue systems which is an important branch in the field of artificial intelligence (AI). However, the evaluation of large-scale Chinese human-computer dialogues is still a challenging task. To attract more attention to dialogue evaluation work, we held the fourth Evaluation of Chinese Human-Computer Dialogue Technology (ECDT). It consists of few-shot learning in spoken language understanding (SLU) (Task 1) and knowledge-driven multi-turn dialogue competition (Task 2), the data sets of which are provided by Harbin Institute of Technology and Tsinghua University. In this paper, we will introduce the evaluation tasks and data sets in detail. Meanwhile, we will also analyze the evaluation results and the existing problems in the evaluation.

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.

scholarly journals Budget Funding Priorities and Development Prospects of the US Artificial Intelligence

2021 ◽  
pp. 65-93
Author(s):  
Y. Selyanin
Keyword(s):  
Artificial Intelligence ◽  
National Security ◽  
Department Of Defense ◽  
Large Scale ◽  
Political Leadership ◽  
Intelligence Community ◽  
Existing Problems ◽  
The Us ◽  
Us Government ◽  
Development Prospects

The US Government has initiated a large-scale activity on artificial intelligence (AI) development and implementation. Numerous departments and agencies including the Pentagon, intelligence community and citizen agencies take part in these efforts. Some of them are responsible for technology, materials and standards development. Others are customers of AI. State AI efforts receive significant budget funding. Moreover, Department of Defense costs on AI are comparable with the whole non-defense funding. American world-leading IT companies support state departments and agencies in organizing AI technologies development and implementation. The USA's highest military and political leadership supports such efforts. Congress provides significant requested funding. However leading specialists criticize the state's approach to creating and implementing AI. Firstly, they consider authorized assignments as not sufficient. Secondly, even this funding is used ineffectively. Therefore Congress created National Security Commission on Artificial Intelligence (NSCAI) in 2018 for identifying problems in the AI area and developing solutions. This article looks at the stakeholders and participants of the state AI efforts, the budget funding authorization, the major existing problems and the NSCAI conclusions regarding the necessary AI funding in FYs 2021-2032.

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 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.

scholarly journals Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052110001
Author(s):  
Lushun Jiang ◽  
Zhe Wu ◽  
Xiaolan Xu ◽  
Yaqiong Zhan ◽  
Xuehang Jin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Large Scale ◽  
Medical Industry ◽  
Medical Field ◽  
Current Application ◽  
Brain Science ◽  
Existing Problems ◽  
Design And Implementation ◽  
Process Framework ◽  
Problem Solving Strategies

Recent advancements in the field of artificial intelligence have demonstrated success in a variety of clinical tasks secondary to the development and application of big data, supercomputing, sensor networks, brain science, and other technologies. However, no projects can yet be used on a large scale in real clinical practice because of the lack of standardized processes, lack of ethical and legal supervision, and other issues. We analyzed the existing problems in the field of artificial intelligence and herein propose possible solutions. We call for the establishment of a process framework to ensure the safety and orderly development of artificial intelligence in the medical industry. This will facilitate the design and implementation of artificial intelligence products, promote better management via regulatory authorities, and ensure that reliable and safe artificial intelligence products are selected for application.

scholarly journals Cross-Domain Slot Filling as Machine Reading Comprehension

2021 ◽  
Author(s):  
Mengshi Yu ◽  
Jian Liu ◽  
Yufeng Chen ◽  
Jinan Xu ◽  
Yujie Zhang
Keyword(s):  
Reading Comprehension ◽  
Large Scale ◽  
Training Data ◽  
Dialogue Systems ◽  
Domain Specific ◽  
Cross Domain ◽  
New Perspective ◽  
Task Oriented ◽  
Slot Filling ◽  
Machine Reading

With task-oriented dialogue systems being widely applied in everyday life, slot filling, the essential component of task-oriented dialogue systems, is required to be quickly adapted to new domains that contain domain-specific slots with few or no training data. Previous methods for slot filling usually adopt sequence labeling framework, which, however, often has limited ability when dealing with the domain-specific slots. In this paper, we take a new perspective on cross-domain slot filling by framing it as a machine reading comprehension (MRC) problem. Our approach firstly transforms slot names into well-designed queries, which contain rich informative prior knowledge and are very helpful for the detection of domain-specific slots. In addition, we utilize the large-scale MRC dataset for pre-training, which further alleviates the data scarcity problem. Experimental results on SNIPS and ATIS datasets show that our approach consistently outperforms the existing state-of-the-art methods by a large margin.

TO THE PROBLEMS OF COMBATING CORRUPTION IN MODERN RUSSIA AND INTERNATIONAL COOPERATION IN THE FIELD OF ANTI-CORRUPTION

2020 ◽  
Author(s):  
Angela Dranishnikova
Keyword(s):  
Large Scale ◽  
The State ◽  
Point Of View ◽  
Modern Life ◽  
Existing Problems ◽  
Moral Point ◽  
Material Goods ◽  
The Russian Federation ◽  
The Difference ◽  
The Government

In the article, the author reflects the existing problems of the fight against corruption in the Russian Federation. He focuses on the opacity of the work of state bodies, leading to an increase in bribery and corruption. The topic we have chosen is socially exciting in our days, since its significance is growing on a large scale at all levels of the investigated aspect of our modern life. Democratic institutions are being jeopardized, the difference in the position of social strata of society in society’s access to material goods is growing, and the state of society is suffering from the moral point of view, citizens are losing confidence in the government, and in the top officials of the state.

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