A survey on near-human conversational agents

Singh, Satwinder; Beniwal, Himanshu

doi:10.1016/j.jksuci.2021.10.013

Cited by 31 publications

(22 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Features 6 and 7 are used to determine if the candidate sentence matches the military document names and units in the Military Dictionary; the results are displayed as boolean values. Feature 8 represents the longest common subsequence (LCS) between the user query and candidate sentences, as shown in Equation (5). Feature 9 denotes the similarity between the user query and candidate sentence, as shown in Equation (6).…”

Section: Response Generationmentioning

confidence: 99%

“…Task-oriented conversational agents, in particular, are of great interest to many researchers. According to a 2018 VentureBeat article [5] over 300,000 chatbots are operating on Facebook. In addition, a 2021 Userlike survey showed that 68% of consumers liked that chatbots can provide fast answers or responses [6].As a result, text-based conversational systems or chatbots have become increasingly common in everyday life.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Conversational AI over Military Scenarios Using Intent Detection and Response Generation

Chuang

Cheng

2022

Applied Sciences

View full text Add to dashboard Cite

With the rise of artificial intelligence, conversational agents (CA) have found use in various applications in the commerce and service industries. In recent years, many conversational datasets have becomes publicly available, most relating to open-domain social conversations. However, it is difficult to obtain domain-specific or language-specific conversational datasets. This work focused on developing conversational systems based on the Chinese corpus over military scenarios. The soldier will need information regarding their surroundings and orders to carry out their mission in an unfamiliar environment. Additionally, using a conversational military agent will help soldiers obtain immediate and relevant responses while reducing labor and cost requirements when performing repetitive tasks. This paper proposes a system architecture for conversational military agents based on natural language understanding (NLU) and natural language generation (NLG). The NLU phase comprises two tasks: intent detection and slot filling. Detecting intent and filling slots involves predicting the user’s intent and extracting related entities. The goal of the NLG phase, in contrast, is to provide answers or ask questions to clarify the user’s needs. In this study, the military training task was when soldiers sought information via a conversational agent during the mission. In summary, we provide a practical approach to enabling conversational agents over military scenarios. Additionally, the proposed conversational system can be trained by other datasets for future application domains.

show abstract

Section: Response Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Conversational AI over Military Scenarios Using Intent Detection and Response Generation

Chuang

Cheng

2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Moreover, the research in [8] examined previous articles which showed that the personalized learning framework of chatbots helped students improve in their studies. Although the surveys in [9], [10], [11], [12] were careful investigations, they have different aspects than this SLR. For example, the study in [11] used a different database and selection criteria.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the study in [11] used a different database and selection criteria. Also, the studies in [9] and [12] presented different research questions. In addition, the evaluation measures and challenges of implementation are not highlighted in [10].…”

Section: Introductionmentioning

confidence: 99%

English and Arabic Chatbots: A Systematic Literature Review

Alsheddi¹,

Alhenaki²

2022

IJACSA

View full text Add to dashboard Cite

In recent years, the availability of chatbot applications has increased substantially with the advancement of artificial intelligence techniques, and research efforts have been active in the English language, which presents state-of-the-art solutions. However, despite the popularity of the Arabic language, its research community is still in an immature stage. Therefore, the main objective of this systematic literature review is studying state-of-the-art researchfor both the English and Arabic languagesto answer the proposed research questions regarding the development approaches, application domains, evaluation metrics, and development challenges of chatbot applications. The findings show that researchers have devoted more attention to the education domain using retrieval-based approaches while the generation-based approach has grown in popularity recently for providing new responses tasks. Whereas the hybrid approach for ranking multi-possible responses of combining both previous approaches shows a performance improvement. Besides, most metrics used to evaluate chatbot performance are human-based, followed by bilingual evaluation understudy and accuracy metrics. However, defining a common framework for evaluating chatbots remains a challenge. Finally, the open problems and future directions are highlighted to help in developing chatbots with minimal human interference to simulate natural conversations.

show abstract

“…In this branch, work on designing languages to allow communication among a group of artificial agents has been primarily dominated by the multi-agent literature [4] and more recently the swarm systems literature [5]. When a human interacts with an AI, conversational AI [6,7], chatbots and Questions and Answer (Q&A) systems [8] dominate the recent literature using data-driven approaches and neural-learning [9]. The third research direction shifts focus away from human-design of the communication language to the emergence of communication and language in a group of agents.…”

Section: Introductionmentioning

confidence: 99%

JSwarm: A Jingulu-Inspired Human-AI-Teaming Language for Context-Aware Swarm Guidance

Abbass

Petraki²,

Hunjet³

2022

Front. Phys.

View full text Add to dashboard Cite

Bi-directional communication between humans and swarm systems begs for efficient languages to communicate information between the humans and the Artificial Intelligence (AI)-enabled agents in a manner that is most appropriate for the context. We discuss the criteria for effective teaming and functional bi-directional communication between humans and AI, and the design choices required to create effective languages. We then present a human-AI-teaming communication language inspired by the Australian Aboriginal language of Jingulu, which we call JSwarm. We present the motivation and structure of the language. An example is used to demonstrate how the language operates for a shepherding swarm guidance task.

show abstract

A survey on near-human conversational agents

Cited by 31 publications

References 23 publications

Conversational AI over Military Scenarios Using Intent Detection and Response Generation

Conversational AI over Military Scenarios Using Intent Detection and Response Generation

English and Arabic Chatbots: A Systematic Literature Review

JSwarm: A Jingulu-Inspired Human-AI-Teaming Language for Context-Aware Swarm Guidance

Contact Info

Product

Resources

About