Designing models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so.

We explore the relationship between question answering and constraint relaxation in spoken dialogue systems and develop dialogue strategies for selecting and presenting information succinctly. In particular, we describe methods for dealing with the results of database queries in information-seeking dialogues. Our goal is to structure the dialogue in such a way that the user is neither overwhelmed with information nor left uncertain as to how to refine the query further. We present two sets of evaluation results for a restaurant selection task: one is a system performance evaluation experiment involving twenty subjects, the other is an experimental evaluation of the use of suggestions involving sixteen subjects.

We describe an interactive question answering system, HITIQA, which helps users find answers to complex analytical problems. Such problems often necessitate the user to submit not one but an entire series of questions, both simple and complex, and then to negotiate the final content and form of the answer. HITIQA advances research in human–computer dialogue by enabling topical, mixed initiative interaction over unstructured data. HITIQA uses the process of text framing to bring a level of semantic representation to open-domain data in order to facilitate meaningful dialogue with the user. In this paper we give an overview of HITIQA's design and explain the workings of its main components with particular attention given to its dialogue capabilities. We also present results of end-to-end system evaluations that demonstrate the effectiveness of the system as a whole, as well as contributions of the individual components and specifically the benefits of our dialogue-based approach. While our research continues, a number of HITIQA prototypes have recently been deployed at various government agencies where they are being tested under real operational conditions.

One of the basic topics of question answering (QA) dialogue systems is how follow-up questions should be interpreted by a QA system. In this paper, we shall discuss our experience with the IMIX and Ritel systems, for both of which a follow-up question handling scheme has been developed, and corpora have been collected. These two systems are each other's opposites in many respects: IMIX is multimodal, non-factoid, black-box QA, while Ritel is speech, factoid, keyword-based QA. Nevertheless, we will show that they are quite comparable, and that it is fruitful to examine the similarities and differences. We shall look at how the systems are composed, and how real, non-expert, users interact with the systems. We shall also provide comparisons with systems from the literature where possible, and indicate where open issues lie and in what areas existing systems may be improved. We conclude that most systems have a common architecture with a set of common subtasks, in particular detecting follow-up questions and finding referents for them. We characterise these tasks using the typical techniques used for performing them, and data from our corpora. We also identify a special type of follow-up question, the discourse question, which is asked when the user is trying to understand an answer, and propose some basic methods for handling it.