Chatbots (aka conversational agents, spoken dialogue systems) allow users to interface with computers using natural language by simply asking questions or issuing commands. Given a query, the chatbot builds a semantic representation of the input, transforms it into a logical statement, and performs all the necessary actions to fulfill the user's intent. Sometimes this simply means calculating an exact answer or retrieving a fact from a database, whereas other times it means building a contextual model and running a full-fledged conversation flow while keeping track of anaphoras and cross-references. Besides the direct applications of chatbots in IoT (Amazon’s Alexa, Apple's Siri) and IT (the historical field of Information Retrieval as a whole can be seen as a sub-problem of spoken dialogue systems), chatbots' main appeal for technologists is their location at the intersection of all major Natural Language Processing technologies and many of the deepest questions in Cognitive Science today: semantic parsing, entity recognition, knowledge representation, and coreference resolution. In this talk, I will explore those questions in the context of an applied industry setting, and I will introduce a framework suitable for addressing them, together with an overview of the state-of-the-art in chatbot technology and some original techniques.


Jordi Carrera Ventura is an NLP engineer and computational linguist with over 10 years of experience in both knowledge-based and data-driven technologies. Most of Jordi’s work has focused on multilingual NLP pipelines and methods for extracting structure from unstructured data. He has covered a wide range of areas including machine translation (ProMT, WebInterpret), automatic summarization (Sumplify), chatbots (Maluuba, Telefónica), spell-checking (Grammarly), and sentiment analysis (AYLIEN). Jordi has a passion for data formalization, experimental evaluation, and error-driven development.