Towards responsive Sensitive Artificial Listeners

This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness

Open-domain neural conversational agents: The step towards artificial general intelligence

Development of conversational agents started half century ago and since then it has transformed into a technology that is accessible in various aspects in everyday life. This paper presents a survey current state-of-the-art in the open domain neural conversational agent research and future research directions towards Artificial General Intelligence (AGI) creation. In order to create a conversational agent which is able to pass the Turing Test, numerous research efforts are focused on open-domain dialogue system. This paper will present latest research in domain of Neural Network reasoning and logical association, sentiment analysis and real-time learning approaches applied to open domain neural conversational agents. As an effort to provide future research directions, current cuttingedge approaches applied to open domain neural conversational agents, current cutting-edge approaches in rationale generation and the state-of-the-art research directions in alternative training methods will be discussed in this paper

Bias and Fairness in Chatbots: An Overview

Chatbots have been studied for more than half a century. With the rapid development of natural language processing (NLP) technologies in recent years, chatbots using large language models (LLMs) have received much attention nowadays. Compared with traditional ones, modern chatbots are more powerful and have been used in real-world applications. There are however, bias and fairness concerns in modern chatbot design. Due to the huge amounts of training data, extremely large model sizes, and lack of interpretability, bias mitigation and fairness preservation of modern chatbots are challenging. Thus, a comprehensive overview on bias and fairness in chatbot systems is given in this paper. The history of chatbots and their categories are first reviewed. Then, bias sources and potential harms in applications are analyzed. Considerations in designing fair and unbiased chatbot systems are examined. Finally, future research directions are discussed

Deep Learning-Based Speech Emotion Recognition Using Librosa

Speech Emotion Recognition is a challenge of computational paralinguistic and speech processing that tries to identify and classify the emotions expressed in spoken language. The objective is to infer from a speaker's speech patterns, such as prosody, pitch, and rhythm, their emotional state, such as happiness, rage, sadness, or frustration. In the modern world, one of the most crucial marketing tactics is emotion detection. For a person, you might tailor several things in order to best fit their interests. Due to this, we made the decision to work on a project where we could identify a person's emotions based just on their speech, allowing us to handle a variety of AI-related applications. Examples include the ability of call centers to play music during tense exchanges. Another example might be a smart automobile that slows down when someone is scared or furious. In Python, we processed and extracted features from the audio files using the Librosa module. A Python library for audio and music analysis is called Librosa. It offers the fundamental components required to develop systems for retrieving music-related information. Because of this, there is a lot of potential for this kind of application in the market that would help businesses and ensure customer safety

Applications of Artificial Intelligence in the Treatment of Behavioral and Mental Health Conditions

Introduction Artificial intelligence (AI) is the branch of science that studies and designs intelligent devices. For individuals unfamiliar with artificial intelligence, the concept of intelligent machines may bring up visions of attractive human-like computers or robots, like those described in science fiction. Others may consider AI technology to be mysterious machines limited to research facilities or a technical triumph that will come in the far future. Popular media accounts on the deployment of aerial drones, autonomous autos, or the potential dangers of developing super-intelligent technologies may have raised some broad awareness of the subject

Defining a New NLP Playground

The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history. This has resulted in concerns that the field will become homogenized and resource-intensive. The new status quo has put many academic researchers, especially PhD students, at a disadvantage. This paper aims to define a new NLP playground by proposing 20+ PhD-dissertation-worthy research directions, covering theoretical analysis, new and challenging problems, learning paradigms, and interdisciplinary applications.Comment: EMNLP Findings 2023 "Theme Track: Large Language Models and the Future of NLP

TikTalk: A Video-Based Dialogue Dataset for Multi-Modal Chitchat in Real World

To facilitate the research on intelligent and human-like chatbots with multi-modal context, we introduce a new video-based multi-modal dialogue dataset, called TikTalk. We collect 38K videos from a popular video-sharing platform, along with 367K conversations posted by users beneath them. Users engage in spontaneous conversations based on their multi-modal experiences from watching videos, which helps recreate real-world chitchat context. Compared to previous multi-modal dialogue datasets, the richer context types in TikTalk lead to more diverse conversations, but also increase the difficulty in capturing human interests from intricate multi-modal information to generate personalized responses. Moreover, external knowledge is more frequently evoked in our dataset. These facts reveal new challenges for multi-modal dialogue models. We quantitatively demonstrate the characteristics of TikTalk, propose a video-based multi-modal chitchat task, and evaluate several dialogue baselines. Experimental results indicate that the models incorporating large language models (LLM) can generate more diverse responses, while the model utilizing knowledge graphs to introduce external knowledge performs the best overall. Furthermore, no existing model can solve all the above challenges well. There is still a large room for future improvements, even for LLM with visual extensions. Our dataset is available at \url{https://ruc-aimind.github.io/projects/TikTalk/}.Comment: Accepted to ACM Multimedia 202