Policy learning is an active topic in dialogue systems research, but it has not been explored in relation to interactive question answering (IQA). We take a first step in learning adaptive interaction policies for question answering : we address the question of how to acquire enough reliable query constraints, how many database results to present to the user and when to present them, given the competing trade-offs between the length of the answer list, the length of the interaction, the type of database and the noise in the communication channel. The operating conditions are reflected in an objective function which we use to derive a hand-coded threshold-based policy and rewards to train a reinforcement learning policy. The same objective function is used for evaluation. We show that we can learn strategies for this complex trade-off problem which perform significantly better than a variety of hand-coded policies, for a wide range of noise conditions, user types, types of DB and turn-penalties. Our policy learning framework thus covers a wide spectrum of operating conditions. The learned policies produce an average relative increase in reward of 86.78% over the hand-coded policies. In 93% of the cases the learned policies perform significantly better than the hand-coded ones (p < .001). Furthermore we show that the type of database has a significant effect on learning and we give qualitative descriptions of the learned IQA policies.

Evaluating interactive question answering (QA) systems with real users can be challenging because traditional evaluation measures based on the relevance of items returned are difficult to employ since relevance judgments can be unstable in multi-user evaluations. The work reported in this paper evaluates, in distinguishing among a set of interactive QA systems, the effectiveness of three questionnaires: a Cognitive Workload Questionnaire (NASA TLX), and Task and System Questionnaires customized to a specific interactive QA application. These Questionnaires were evaluated with four systems, seven analysts, and eight scenarios during a 2-week workshop. Overall, results demonstrate that all three Questionnaires are effective at distinguishing among systems, with the Task Questionnaire being the most sensitive. Results also provide initial support for the validity and reliability of the Questionnaires.

In this introduction, we present our overview of interactive question answering (IQA). We contextualize IQA in the wider field of question answering, and establish connections to research in Information Retrieval and Dialogue Systems. We highlight the development of QA as a field, and identify challenges in the present research paradigm for which IQA is a potential solution. Finally, we present an overview of papers in this special issue, drawing connections between these and the challenges they address.

We report work on adding semantic role labels to the Chinese Treebank, a corpus already annotated with phrase structures. The work involves locating all verbs and their nominalizations in the corpus, and semi-automatically adding semantic role labels to their arguments, which are constituents in a parse tree. Although the same procedure is followed, different issues arise in the annotation of verbs and nominalized predicates. For verbs, identifying their arguments is generally straightforward given their syntactic structure in the Chinese Treebank as they tend to occupy well-defined syntactic positions. Our discussion focuses on the syntactic variations in the realization of the arguments as well as our approach to annotating dislocated and discontinuous arguments. In comparison, identifying the arguments for nominalized predicates is more challenging and we discuss criteria and procedures for distinguishing arguments from non-arguments. In particular we focus on the role of support verbs as well as the relevance of event/result distinctions in the annotation of the predicate-argument structure of nominalized predicates. We also present our approach to taking advantage of the syntactic structure in the Chinese Treebank to bootstrap the predicate-argument structure annotation of verbs. Finally, we discuss the creation of a lexical database of frame files and its role in guiding predicate-argument annotation. Procedures for ensuring annotation consistency and inter-annotator agreement evaluation results are also presented.

Interactive question answering (QA), where a dialogue interface enables follow-up and clarification questions, is a recent although long-advocated field of research. We report on the design and implementation of YourQA, our open-domain, interactive QA system. YourQA relies on a Web search engine to obtain answers to both fact-based and complex questions, such as descriptions and definitions. We describe the dialogue moves and management model making YourQA interactive, and discuss the architecture, implementation and evaluation of its chat-based dialogue interface. Our Wizard-of-Oz study and final evaluation results show how the designed architecture can effectively achieve open-domain, interactive QA.

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.