Commercial Applications through Community Question Answering Technology
Authors: Antonio Uva, Valerio Storch, Casimiro Carrino, Ugo Di Iorio, Alessandro Moschitti (Università di Trento, Qatar Computing Research Institute, RGI Group, 2017)
Topic: Chatbots and Dialogue Systems

Abstract
In this paper, we describe our experience on using current methods developed for Community Question Answering (cQA) for a commercial application focused on an Italian help desk. Our approach is based on (i) a search engine to retrieve previously answered question candidates and (ii) kernel methods applied to advanced linguistic structures to rerank the most promising candidates. We show that methods developed for cQA work well also when applied to data generated in customer service scenarios, where the user seeks for explanation about products and a database of previously answered questions is available. The experiments with our system demonstrate its suitability for an industrial scenario.

Introduction
In recent years, open-domain Question Answering (QA) has been more and more used by large companies, e.g., IBM, Google, Facebook, Microsoft, etc., for their commercial applications. However, medium and smaller enterprises typically cannot invest billions of dollars in achieving the desired QA accuracy: this limits the use of this technology, especially, in case of less supported languages, e.g., Italian. One viable alternative for smaller companies is the design of close-domain systems looking for answers in specific data. For example, most companies require to quickly and accurately search their own documentation or the one of their customers, which are often available in terms of unstructured text. However, even this scenario is complicated as reaching the a satisfactory accuracy may require a lot of resources. An interesting alternative is provided by cQA technology, which uses techniques tailored for answering questions in specific forums. In addition, to the intuitive observation that the forum topics are rather restricted, making the retrieval task easier, cQA offers an even more interesting property: when a new question is asked in a forum, instead of searching for an answer, the system tries to look for a similar question. Indeed, similar questions were asked before and may have received answers, thus the system can provide the users with such responses. The main advantage of this approach is that searching for similar questions is much easier than searching for text answering a given question. \antonio{Due to this, challenges such as SemEval-2017 Task 3 AUTHOR and QA4FAQ AUTHOR, aimed at testing current cQA available technology, have been organized}. In this paper, we show that help desk applications, generally required by most companies, can adopt the cQA model to automatize the answering process. In particular, we describe our QA system developed for RGI, which is a software vendor specialized in the insurance businesses. One important task carried out by their help desk software regards answering customers' questions using a ticket system. Already answered tickets are stored in specialized databases but manually finding and routing them to the users is time consuming. We show that our approach, using standard search engines and advanced reranker based on machine learning and NLP technology, can achieve answer recall of almost 85% when considering the top three retrieved tickets. This is particularly interesting because the experimented data and models are completely in Italian, demonstrating the maturity of this technology also for this language.

Conclusion
In this paper, we have described our experience in building a QA model for an Italian help desk in the field of insurance policies. Our main findings are: (i) the Italian NLP technology seems enough accurate to support advanced cQA technology based on syntactic structures; (ii) cQA model can boost the retrieval systems targeting text in Italian; and (iii) the achieved accuracy seems appropriate to create business at least in the filed of help desk applications, although it should be considered that our results refer to only questions having an answer in our database.