Gimme the Usual - How Handling of Pragmatics Improves Chatbots
Authors: Alessia Bianchini, Francesco Tarasconi, Raffaella Ventaglio, Mariafrancesca Guadalupi (Università di Torino, CELI Language Technology, Gruppo Anchise, 2017)
Topic: Chatbots and Dialogue Systems

Abstract
We provide our view on the components needed for both the development and further improvement of robust and effective chatbots. We focus on why Pragmatics is important in developing next generation chatbots by bringing a few generalizable examples. We report our current experience on the design and implementation of a task-oriented textual chatbot for a closed-domain Question Answering system, which tackles problems in Pragmatics.

Introduction
Chatbot, chatterbot, natural-language interface, dialogue system are some of the terms used to re- fer to softwares that aim to carry on conversations with humans (AUTHOR; LAUTHOR; AUTHOR). We will not go into further details about the classification and definition of such softwares. We will use chatbot as if it was a hypernym of the above mentioned softwares instead. Chatbots and Intent Understanding. The goal of an intelligent chatbot is to understand the user’s intent AUTHOR and behave accordingly. Such goal is quite complex to achieve, and beyond the capability of current state of the art chatbots. However, the hype around chatbots has raised awareness of what elements are needed for a chatbot to manage human-like interactions. It is generally agreed that to build effective and solid chatbots the following is needed: Natural Language Processing (NLP): much of the intelligence needed to understand human intent lies in the processing of human language. Hence, the development and improvement of NLP algorithms is a necessary prerequisite for the creation of intelligent chatbots. Machine Learning (ML): chatbot design should rely on ML for learning and automatically con- solidating NLP rules by means of observation of past experience - i.e., past conversations and their outcomes AUTHOR. Current chatbot development, given enough annotated data, should consider adopting recently developed algorithms that are task-oriented (AUTHOR) or topic aware (AUTHOR). Developments in reinforcement learning applications seem promising for task-oriented dialogue systems AUTHOR. Context and State Awareness: depending on the purpose of the chatbot, the component responsible for the managing of the conversation (Dialogue Manager System - DMS) should take into account both context and states variables AUTHOR. From the DMS point of view, chatbots are usually classified as: stateless chatbots; semi-stateful chatbots; stateful chatbots (next generation chat- bots). During the conversation, state transitions depend on the information acquired before. As for the follow up action, it depends on the recognized context. Natural Language Generation (NLG): NLG concerns what information and in what form it should be delivered AUTHOR. Dealing with "real" conversation requires being both proactive (e.g. suggest the best option; drive along the compilation of a form; remind planned activities; ...) AUTHOR and adaptive (e.g. change style - both in written and spoken scenarios - according to domain, mood of the user, or sociolinguistic variables). We argue here that, in addition to the above mentioned, moving from Semantics to Pragmatics plays a crucial role in building chatbots. This is because a lot of the knowledge human beings share during a conversation gets constructed along the conversation itself (AUTHOR; AUTHOR). For instance, let’s consider the following mock dialogue between Human (H) and Chatbot (C): H lands on a money transfer service page. H: Hello, I would like to make a transfer C: Hello. Sure. Would you like to know more about: [FAQ menu about transfer service is shown]? H navigates the FAQ menu H signs up online to proceed with the transfer. H: I would like to make a transfer C: Sure. It only takes a couple of minutes C starts the procedure to execute the transfer. In this interaction, the sentence “I would like to make a transfer” instantiates two different in- tents: informative intent, at first (H is looking for information about the transfer service); follow up intent, then (once H is satisfied with transfer service conditions, he/she wants to proceed with the transfer). Such pragmatic disambiguation involves taking knowledge from the conversational context into account, which is one of the most difficult tasks for a chatbot. We report below how we deal with this task in our task-oriented closed- domain chatbot.

Conclusion
We are currently in the process of evaluating Financial QA according to a framework based on PARADISE (AUTHOR; AUTHOR), which considers, among the others, the following indicators: Task Ease, NLU Performance, Expected Behavior, Presentation, Verbal Presentation, Future Use. We plan to finalize our evaluation in the next months. NLP is crucial for the development of robust chatbots; since extra-linguistic elements are potenTially very important in intent understanding, moving from semantics to pragmatics is a necessary step to develop next-generation chatbots. We have shown how Dialogue Management can support a more robust handling of context, at least in closed- domain QA tasks. Further work is required to handle more business cases and a broader definition of context, such as history of activities conducted by the same user, which can be especially useful in chatbots with recommender functions AUTHOR. We would like to thank Andrea Bolioli for his help in the review phase and all of our CELI colleagues for their invaluable work and support.