In this survey paper, we overview major deep learning methods used in Natural Language Processing (NLP) and source code over the last 35 years. Next, we present a survey of the applications of Artificial Intelligence (AI) for source code, also known as Code Intelligence (CI) and Programming Language Processing (PLP). We survey over 287 publications and present a software-engineering centered taxonomy for CI placing each of the works into one category describing how it best assists the software development cycle. Then, we overview the field of conversational assistants and their applications in software engineering and education. Lastly, we highlight research opportunities at the intersection of AI for code and conversational assistants and provide future directions for researching conversational assistants with CI capabilities.
Research on emotionally intelligent dialogue generation based on automatic dialogue system
University of the People（人民大学）；Sehan University（世翰大学）；Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）；Universidad Internacional Isabel I de Castilla（卡斯蒂利亚伊莎贝尔一世国际大学）；AMA University（AMA大学）
The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on method…
Faculty of Electrical Engineering, CTU Prague（布拉格捷克理工大学电气工程学院）；CTU Medialab（布拉格捷克理工大学媒体实验室）；DTU Compute, Technical University of Denmark（丹麦技术大学计算学院）；CIIRC, CTU Prague（布拉格捷克理工大学人工智能、机器人与控制论研究所）
The open domain-dialogue system Alquist has a goal to conduct a coherent and engaging conversation that can be considered as one of the benchmarks of social intelligence. The fourth version of the system, developed within the Alexa Prize Socialbot Grand Challenge 4, brings two main innovations. The first addresses coherence, and the second addresses the engagingness of the conversation. For innovations regarding coherence, we propose a novel hybrid approach combining hand-designed responses and a generative model. The proposed approach utilizes hand-designed dialogues, out-of-domain detection,…
Recognition of social signals, from human facial expressions or prosody of speech, is a popular research topic in human-robot interaction studies. There is also a long line of research in the spoken dialogue community that investigates user satisfaction in relation to dialogue characteristics. However, very little research relates a combination of multimodal social signals and language features detected during spoken face-to-face human-robot interaction to the resulting user perception of a robot. In this paper we show how different emotional facial expressions of human users, in combination w…
University of Waterloo（滑铁卢大学）；Huawei Technologies Co., Ltd.（华为技术有限公司）
For conversational AI and virtual assistants to communicate with humans in a realistic way, they must exhibit human characteristics such as expression of emotion and personality. Current attempts toward constructing human-like dialogue agents have presented significant difficulties. We propose Human Level Attributes (HLAs) based on tropes as the basis of a method for learning dialogue agents that can imitate the personalities of fictional characters. Tropes are characteristics of fictional personalities that are observed recurrently and determined by viewers' impressions. By combining detailed…
Cognotekt GmbH（科格诺泰克有限公司）；University at Buffalo（布法罗大学）
The goal of creating Artificial General Intelligence (AGI) -- or in other words of creating Turing machines (modern computers) that can behave in a way that mimics human intelligence -- has occupied AI researchers ever since the idea of AI was first proposed. One common theme in these discussions is the thesis that the ability of a machine to conduct convincing dialogues with human beings can serve as at least a sufficient criterion of AGI. We argue that this very ability should be accepted also as a necessary condition of AGI, and we provide a description of the nature of human dialogue in pa…
In this article, we first give an introduction to artificial intelligence and its applications in biology and medicine in Section 1. Deep learning methods are then described in Section 2. We narrow down the focus of the study on textual data in Section 3, where natural language processing and its applications in the biomedical domain are described. In Section 4, we give an introduction to explainable artificial intelligence and discuss the importance of explainability of artificial intelligence systems, especially in the biomedical domain.
This volume represents the accepted submissions from the AAAI-2019 Workshop on Games and Simulations for Artificial Intelligence held on January 29, 2019 in Honolulu, Hawaii, USA. https://www.gamesim.ai
EiCAP: Beyond Fluency, Probing and Improving Emotional Intelligence in LLMs via Psychologically Grounded Multi-Turn Dialogue
Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University（卡塔尔计算研究所（QCRI），哈马德·本·卡伊夫大学）；University of Illinois Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Institute of Technology（北京理工大学）；Xi’an Jiaotong University（西安交通大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
We describe Panacea, a system that supports natural language processing (NLP) components for active defenses against social engineering attacks. We deploy a pipeline of human language technology, including Ask and Framing Detection, Named Entity Recognition, Dialogue Engineering, and Stylometry. Panacea processes modern message formats through a plug-in architecture to accommodate innovative approaches for message analysis, knowledge representation and dialogue generation. The novelty of the Panacea system is that uses NLP for cyber defense and engages the attacker using bots to elicit evidenc…
INESC-ID（葡萄牙国家系统与计算机工程研究所）；Faculdade de Ciências e Tecnologia, Universidade do Algarve（阿尔加夫大学科学与技术学院）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Faculdade de Ciências Sociais e Humanas, Universidade do Algarve（阿尔加夫大学社会与人文科学学院）；Imprensa Nacional Casa da Moeda（国家铸币印刷局）
While Artificial Intelligence applied to the legal domain is a topic with origins in the last century, recent advances in Artificial Intelligence are posed to revolutionize it. This work presents an overview and contextualizes the main advances on the field of Natural Language Processing and how these advances have been used to further the state of the art in legal text analysis.
University of Auckland（奥克兰大学）；NHN Diquest（NHN Diquest公司）；Yonsei University（延世大学）
Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days. However, artificial intelligence-related patents are very difficult to classify because it is a mixture of complex technologies and legal terms. Moreover, due to the unsatisfactory performance of current algorithms, it is still mostly done manually, wasting a lot of time and money. Therefore, we present a method for classifying artificial intelligence-related patents published…
We examine how much of the contemporary progress in artificial intelligence (and, specifically, in natural language processing), can be, more or less directly, traced back to the seminal work and ideas of the Austrian-British philosopher Ludwig Wittgenstein, with particular focus on his late views. Discussing Wittgenstein's original theses will give us the chance to survey the state of artificial intelligence, and comment on both its strengths and weaknesses. A similar text appeared first in Spanish as a chapter of CENTENARIO DEL SILENCIO (2021), a book celebrating 100 years since the publicat…
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland Informatics Campus（萨尔兰信息学园区）
Current advances in machine translation increase the need for translators to switch from traditional translation to post-editing of machine-translated text, a process that saves time and improves quality. Human and artificial intelligence need to be integrated in an efficient way to leverage the advantages of both for the translation task. This paper outlines approaches at this boundary of AI and HCI and discusses open research questions to further advance the field.
Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. A diversity of dialogue systems has been designed with the rapid development of deep learning techniques, especially the recent pre-trained language models (PrLMs). Among these studies, the fundamental yet challenging type of task is dialogue comprehension whose role is to teach the machines to read and comprehend the dialogue context before responding. In this paper, we review the previous methods from the technical perspective of dialogue modeling for the dia…
