We describe SCoT, a Spoken Conversational Tutor, which has been implemented in order to investigate the advantages of natural language in tutoring, especially spoken language. SCoT uses a generic architecture for conversational intelligence which has capabilities such as turn management and coordination of multi-modal input and output. SCoT also includes a set of domain independent tutorial rec...

This paper presents a novel methodology of incorporating Information Extraction (IE) techniques into an Enhanced Conversation-Based Interface to Relational Databases (C-BIRD) in order to generate dynamic SQL queries. Conversational Agents can converse with the user in natural language about a specific problem domain. In C-BIRD, such agents allow a user to converse with a relational database in ...

Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this paper, we propose an extension to an LSTM (Long Short-Term Memory) language model for ...

We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoderdecoder based language generation. This capability is desirable in a variety of applications, including conversational systems, where successful agents need to produce language in a specific style and generate responses steered by a human puppeteer or external knowledge. We decom...

As we all know, more and more of life is now manifested online, and many of the digital traces that are left by human activity are increasingly recorded in natural-language format. This availability offers us the opportunity to glean user-modeling information from individual users’ linguistic behaviors. This talk will discuss the particular phenomenon of individual language adaptation, both in ...

Recently there is growing interest in using neural networks for language modeling. In contrast to the well known backoff ngram language models (LM), the neural network approach tries to limit problems from the data sparseness by performing the estimation in a continuous space, allowing by these means smooth interpolations. Therefore this type of LM is interesting for tasks for which only a very...

The main aim of this research is to investigate a novel technique for implementing a conversational intelligent agent system on the web. The research will focus on a multilevel natural language query approach. While the present World Wide Web provides a distributed hypermedia interface to the vast amount of information on the Internet, there is a lack of appropriate metadata to that content. In...

This paper describes our recent work in developing multilingual conversational systems that support human-computer interactions. Our approach is based on the premise that a common semantic representation can be extracted from the input for all languages, at least within the context of restricted domains. In our design of such systems, language dependent information is separated from the system ...