There is no need to say that nowadays we can observe the unprecedentedly increasing trend in multidisciplinary convergence in many fields of science yielding better results and offering promising perspectives for novel breakthroughs and major advances in interdisciplinary fields involved. Such area of linguistic science as sociolinguistics is not an exception. Moreover, it can benefit tremendously from fruitful cooperation with engineering allowing linguists to look at some scientific problems from various perspectives.

It would not be an exaggeration to claim that sociolinguistics has developed significantly thanks to computer technologies. According to Tyler Kendall (2011), technological advancements have been paramount in the development of sociolinguistics. It is difficult to imagine how to store and analyze huge amount of linguistic texts without computerized means (Kendall, 2011).

It is necessary to highlight that sociolinguistics is, as it implies from its name, the study of sociological aspects of language, i.e. diverse social factors such as age, sex, education, race, occupation, social relationships etc. can significantly influence linguistic choice of a speaker. (Sociolinguistics, n.d.). Computational methods applied to sociolinguistics can extend the sociolinguistic research dramatically, help evaluate extensive array of information in less time, provide assistance in compiling corpus for sociolinguistic research as well as comparing use of language across population using various computer programs, etc.

To illustrate this fact it is necessary to mention the SoSweet project held in France to analyze the evolution of the variety of French used on Twitter. The project mentioned above focuses on a corpus of 500 million tweets combined with the social network of the 10 million users who authored these tweets, complemented by socio­demographic data. Due to computational methods from different areas, the SoSweet project was implemented at the crossing of social media linguistics, sociolinguistics, natural language processing and network science. Thus, we can come to conclusion that such a project would not have been possible to imagine without the productive cooperation of sociolinguists and IT engineers working in close collaboration to achieve better results and go beyond traditional decisions in solving multiple problems arising in implementation of the project (SoSweet: A sociolinguistics of Twitter, n.d.).

There seems to be no compelling reason to argue that, unfortunately, at the moment there is not so much research dedicated to computational sociolinguistics. Despite the importance of multidisciplinary convergence of engineering and sociolinguistics mentioned above, the analysis of foreign and native scientific and methodological literature has shown that there is a lack of scientific works devoted to investigation of strategies and methods of sociolinguistics that can be successfully applied to the solution of engineering problems.

However, it is obvious that engineering can gain invaluable knowledge and support from sociolinguists. Let’s speculate upon some areas where tight collaboration of sociolinguists and engineers can give tangible results. It would be worthwhile considering the areas where data collected and analyzed by sociolinguists can be extremely helpful to engineering.

It goes without saying that it is technological innovation that shapes today’s modern world. Nowadays while surfing the net we can observe transition from FAQ to chatbots. As the creation and use of chatbots kick into high gear, sociolinguistics can help tremendously in improving the efficiency of chatbots’ development and work. According to the definition of a chatbot it is an artificial intelligence software that can simulate a conversation or a chat with a user in natural language through messaging applications, websites, mobile apps or through the telephone. (Chatbot: What is a Chatbot?, 2020)

The key phrase in the definition of a chat we need to focus on is natural language. That is the area where sociolinguistic data collected and analyzed can be successfully used and integrated into the development of high quality software programs that would meet customer expectation.

It is necessary to highlight that the task of a chatbot is to analyze the user’s request while identifying the user intent. The ability to identify the user’s intent and extract data and relevant entities contained in the user’s request is the first condition and the most relevant step at the core of a chatbot: If you are not able to correctly understand the user’s request, you won’t be able to provide the correct answer. (Chatbot: What is a Chatbot?, 2020)

That is the point where engineers unavoidably hit a number of stumbling blocks that can hinder the development of a software product of high quality, i.e. the chatbots that can understand any speaker from various strata of society as well as any region of the country. Undoubtedly, we can claim that this is the major problem for English­speaking countries with numerous dialects in addition to slang words and sociolects.

It seems fair to suggest that this issue is of top priority for IT developers as it would be impossible to create effective chatbots without huge collection of information from sociolinguistic corpora. Hence, we come to a conclusion that IT specialists working in close collaboration with sociolinguists and using corpora of sociolects, dialects, slang, etc. are able to develop chatbot applications where all possible linguistic regional and social variations should be paid close and careful attention to while designing the product.

Taking into consideration the deficiency of the system development of methods and approaches to computational sociolinguistics, further scientific research is needed to focus on scrutinising all sorts of possible solutions of the problems arising from the necessity to apply data collected by sociolinguists in the development of chatbot software application. The outcomes of the scientific and practical research mentioned above can be used to identify the perspective directions of studying this problem in the future.

References

1. Chatbot: What is a Chatbot? Why are Chatbots Important? (2020, March 17) Retrieved from: https://www.expert.ai/blog/chatbot/
2. Kendall, T. (2011). Corpora from a sociolinguistic perspective. RBLA, Belo Horizonte, 11(2), 361-389. Retrieved from:
3. https://slaap.chass.ncsu.edu/pdfs/Kendall2011 BJAL CorpSocioling.pdf
4. Sociolinguistics. (n.d.). Encyclopedia Britannica. Retrieved June 20, 2021, from: https://www.britannica.com/science/sociolinguistics
5. SoSweet:  A sociolinguistics of Twitter. (n.d.). Retrieved from: https://sosweet.inria.fr/