Emotional Human-Machine Conversation Generation Based on Long Short-Term Memory

Sun, Xiao; Peng, Xiaoqi; Ding, Shuai

doi:10.1007/s12559-017-9539-4

Cited by 32 publications

(19 citation statements)

References 24 publications

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“…A significant amount of research is still under way to infer human intention in simple human-robot interaction tasks with unsolved problems, never mind complex interaction tasks. The research community is still developing solutions for AI to be, interpretable [2][3][4], transparent and explainable [1] to allow humans to understand the intention of an AI and develop mutual predictability and shared understanding. It is tempting for some to claim that taking the human out AI guides, human performs: AI guiding a pilot to control the workload of air traffic controllers [41].…”

Section: Relationships Of Equals: Why Is Teaming Hard For Ai Agents?mentioning

confidence: 99%

Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust

Abbass

2019

Cogn Comput

109

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Artificial intelligence (AI) is finding more uses in the human society resulting in a need to scrutinise the relationship between humans and AI. Technology itself has advanced from the mere encoding of human knowledge into a machine to designing machines that "know how" to autonomously acquire the knowledge they need, learn from it and act independently in the environment. Fortunately, this need is not new; it has scientific grounds that could be traced back to the inception of computers. This paper uses a multidisciplinary lens to explore how the natural cognitive intelligence in a human could interface with the artificial cognitive intelligence of a machine. The scientific journey over the last 50 years will be examined to understand the Human-AI relationship, and to present the nature of, and the role of trust in, this relationship. Risks and opportunities sitting at the human-AI interface will be studied to reveal some of the fundamental technical challenges for a trustworthy human-AI relationship. The critical assessment of the literature leads to the conclusion that any social integration of AI into the human social system would necessitate a form of a relationship on one level or another in society, meaning that humans will "always" actively participate in certain decision-making loops-either in-the-loop or on-the-loop-that will influence the operations of AI, regardless of how sophisticated it is.

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Section: Relationships Of Equals: Why Is Teaming Hard For Ai Agents?mentioning

confidence: 99%

Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust

Abbass

2019

Cogn Comput

109

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“…The generation of emotional dialogue responses is mainly achieved by learning emotional labels. 22,126 Question answering system, which is focused more on factual questions, can be regarded as a special case of the dialogue system, and it can answer the questions posed by humans with more accurate and concise natural language. There are also plenty of good works in the¯eld.…”

Section: Listening and Speakingmentioning

confidence: 99%

A Review on Human-Computer Interaction and Intelligent Robots

Ren

Bao

2020

Int. J. Info. Tech. Dec. Mak.

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In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research.

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“…The interest, therefore, turns to if empathy could be integrated into neural generation models. For the purpose of this thesis, we have identified five existing publications in this area to review [18,34,90,91,92]. There are three levels at which a neural generation approach can be adapted to incorporate empathy, input, training level or output stages.…”

Section: Empathic Modulementioning

confidence: 99%

“…This is achieved by one of our two approaches, hard-coded labelling or emotional embedding. The authors of [18,91] use various methods to extract hard-coded labels, often choosing six or eight major emotional classes and annotating each input against a class. The embedding of these classes (often just a one-hot encoded vector) is concatenated against the embedding of the input sentence and fed into a standard training model.…”

Section: Empathic Modulementioning

confidence: 99%

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Passive Diagnosis of Mental Health Disorders Incorporating an Empathic Dialogue System

Delahunty¹

2019

Preprint

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Depression and anxiety are the two most prevalent mental health disorders worldwide, impacting the lives of millions of people each year. Current screening methods require individuals to manually complete psychometric questionnaires. In this work we develop a deep learning approach to predict psychometric scores given textual data through the use of psycholinguistics features. Data is collected via a dialogue system, were we develop and incorporate an approach to model empathy. Which aims to allow for appropriate use of these systems in a clinical setting. Following a public evaluation, we demonstrate that our approach to model empathy can out perform a similarly trained non empathic approach. Additionally, we show that our deep learning prediction approach performed well on evaluation data, but has difficulty generalizing to experimentally collected data. Limitations and implications as a result of this work are discussed.

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Emotional Human-Machine Conversation Generation Based on Long Short-Term Memory

Cited by 32 publications

References 24 publications

Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust

Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust

A Review on Human-Computer Interaction and Intelligent Robots

Passive Diagnosis of Mental Health Disorders Incorporating an Empathic Dialogue System

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