scholarly journals Conversational Agent Research Toolkit: An alternative for creating and managing chatbots for experimental research

2019 ◽  
Author(s):  
Theo Araujo
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Experimental Research ◽  
Experimental Studies ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Communication Environment

Conversational agents in the form of chatbots available in messaging platforms are gaining increasing relevance in our communication environment. Based on natural language processing and generation techniques, they are built to automatically interact with users in several contexts. We present here a tool, the Conversational Agent Research Toolkit (CART), aimed at enabling researchers to create conversational agents for experimental studies. CART integrates existing APIs frequently used in practice and provides functionality that allows researchers to create and manage multiple versions of a chatbot to be used as stimuli in experimental studies. This paper provides an overview of the tool and provides a step-by-step tutorial of to design an experiment with a chatbot.

scholarly journals Conversational Agent Research Toolkit : An alternative for creating and managing chatbots for experimental research1

2020 ◽  
Vol 2 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Theo Araujo
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Experimental Studies ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Communication Environment

Abstract Conversational agents in the form of chatbots available in messaging platforms are gaining increasing relevance in our communication environment. Based on natural language processing and generation techniques, they are built to automatically interact with users in several contexts. We present here a tool, the Conversational Agent Research Toolkit (CART), aimed at enabling researchers to create conversational agents for experimental studies. CART integrates existing APIs frequently used in practice and provides functionality that allows researchers to create and manage multiple versions of a chatbot to be used as stimuli in experimental studies. This paper provides an overview of the tool and provides a step-by-step tutorial of to design an experiment with a chatbot.

Natural Language Processing based Human Assistive Health Conversational Agent for Multi-Users

2021 ◽  
Author(s):  
Jim Elliot Christopherjames ◽  
Mahima Saravanan ◽  
Deepa Beeta Thiyam ◽  
Prasath Alias Surendhar S ◽  
Mohammed Yashik Basheer Sahib ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Conversational Agent

Recent Developments in Natural Language Processing

2021 ◽  
Author(s):  
Constantin Orasan ◽  
Ruslan Mitkov
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Positive Impact ◽  
User Generated Content ◽  
Conversational Agents ◽  
Automatic Assessment ◽  
Reading Material ◽  
Multimodal Information ◽  
Recent Developments

Natural Language Processing (NLP) is a dynamic and rapidly developing field in which new trends, techniques, and applications are constantly emerging. This chapter focuses mainly on recent developments in NLP which could not be covered in other chapters of the Handbook. Topics such as crowdsourcing and processing of large datasets, which are no longer that recent but are widely used and not covered at length in any other chapter, are also presented. The chapter starts by describing how the availability of tools and resources has had a positive impact on the field. The proliferation of user-generated content has led to the emergence of research topics such as sarcasm and irony detection, automatic assessment of user-generated content, and stance detection. All of these topics are discussed in the chapter. The field of NLP is approaching maturity, a fact corroborated by the latest developments in the processing of texts for financial purposes and for helping users with disabilities, two topics that are also discussed here. The chapter presents examples of how researchers have successfully combined research in computer vision and natural language processing to enable the processing of multimodal information, as well as how the latest advances in deep learning have revitalized research on chatbots and conversational agents. The chapter concludes with a comprehensive list of further reading material and additional resources.

Living and working in a fourth culture

2019 ◽  
pp. 133-142
Author(s):  
François Grosjean
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Experimental Research ◽  
University Hospital ◽  
English Writing ◽  
Set Up

The author and his family went back to Europe for good, and had to acculturate to a new culture. His boys needed a bit of help during their first years but then things worked out well. The author set up his laboratory and started collaborating with firms involved in natural language processing (NLP). One of the main projects he worked on with his team was an English writing tool and grammar checker for French speakers. He also developed a long-term partnership with the Lausanne University Hospital (CHUV). The author explains how he helped students do experimental research in his laboratory. The chapter ends with some statistics showing how successful the laboratory was over a span of twenty years.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

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