Follow-up question handling in the IMIX and Ritel systems: A comparative study

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

An authoring tool for decision support systems in context questions of ecological knowledge

Decision support systems (DSS) support business or organizational decision-making activities, which require the access to information that is internally stored in databases or data warehouses, and externally in the Web accessed by Information Retrieval (IR) or Question Answering (QA) systems. Graphical interfaces to query these sources of information ease to constrain dynamically query formulation based on user selections, but they present a lack of flexibility in query formulation, since the expressivity power is reduced to the user interface design. Natural language interfaces (NLI) are expected as the optimal solution. However, especially for non-expert users, a real natural communication is the most difficult to realize effectively. In this paper, we propose an NLI that improves the interaction between the user and the DSS by means of referencing previous questions or their answers (i.e. anaphora such as the pronoun reference in “What traits are affected by them?”), or by eliding parts of the question (i.e. ellipsis such as “And to glume colour?” after the question “Tell me the QTLs related to awn colour in wheat”). Moreover, in order to overcome one of the main problems of NLIs about the difficulty to adapt an NLI to a new domain, our proposal is based on ontologies that are obtained semi-automatically from a framework that allows the integration of internal and external, structured and unstructured information. Therefore, our proposal can interface with databases, data warehouses, QA and IR systems. Because of the high NL ambiguity of the resolution process, our proposal is presented as an authoring tool that helps the user to query efficiently in natural language. Finally, our proposal is tested on a DSS case scenario about Biotechnology and Agriculture, whose knowledge base is the CEREALAB database as internal structured data, and the Web (e.g. PubMed) as external unstructured information.This paper has been partially supported by the MESOLAP (TIN2010-14860), GEODAS-BI (TIN2012-37493-C03-03), LEGOLANGUAGE (TIN2012-31224) and DIIM2.0 (PROMETEOII/2014/001) projects from the Spanish Ministry of Education and Competitivity. Alejandro Maté is funded by the Generalitat Valenciana under an ACIF grant (ACIF/2010/298)

Répondre à des questions à réponses multiples sur le Web

Les systèmes de question-réponse renvoient une réponse précise à une question formulée en langue naturelle. Les systèmes de question-réponse actuels, ainsi que les campagnes d'évaluation les évaluant, font en général l'hypothèse qu'une seule réponse est attendue pour une question. Or nous avons constaté que, souvent, ce n'était pas le cas, surtout quand on cherche les réponses sur le Web et non dans une collection finie de documents.Nous nous sommes donc intéressés au traitement des questions attendant plusieurs réponses à travers un système de question-réponse sur le Web en français. Pour cela, nous avons développé le système Citron capable d'extraire des réponses multiples différentes à des questions factuelles en domaine ouvert, ainsi que de repérer et d'extraire le critère variant (date, lieu) source de la multiplicité des réponses. Nous avons montré grâce à notre étude de différents corpus que les réponses à de telles questions se trouvaient souvent dans des tableaux ou des listes mais que ces structures sont difficilement analysables automatiquement sans prétraitement. C'est pourquoi, nous avons également développé l'outil Kitten qui permet d'extraire le contenu des documents HTML sous forme de texte et aussi de repérer, analyser et formater ces structures. Enfin, nous avons réalisé deux expériences avec des utilisateurs. La première expérience évaluait Citron et les êtres humains sur la tâche d'extraction de réponse multiples : les résultats ont montré que Citron était plus rapide que les êtres humains et que l'écart entre la qualité des réponses de Citron et celle des utilisateurs était raisonnable. La seconde expérience a évalué la satisfaction des utilisateurs concernant la présentation de réponses multiples : les résultats ont montré que les utilisateurs préféraient la présentation de Citron agrégeant les réponses et y ajoutant un critère variant (lorsqu'il existe) par rapport à la présentation utilisée lors des campagnes d'évaluation.Question answering systems find and extract a precise answer to a question in natural language. Both current question-answering systems and evaluation campaigns often assume that only one single answeris expected for a question. Our corpus studies show that this is rarely the case, specially when answers are extracted from the Web instead of a frozen collection of documents.We therefore focus on questions expecting multiple correct answers fromthe Web by developping the question-answering system Citron. Citron is dedicated to extracting multiple answers in open domain and identifying theshifting criteria (date, location) which is often the reason of this answer multiplicity Our corpus studies show that the answers of this kind of questions are often located in structures such as tables and lists which cannot be analysed without a suitable preprocessing. Consequently we developed the Kitten software which aims at extracting text information from HTML documents and also both identifying and formatting these structures.We finally evaluate Citron through two experiments involving users. Thefirst experiment evaluates both Citron and human beings on a multipleanswer extraction task: results show that Citron was faster than humans andthat the quality difference between answers extracted by Citron andhumans was reasonable. The second experiment evaluates user satisfaction regarding the presentation of multiple answers: results show that user shave a preference for Citron presentation aggregating answers and adding the shifting criteria (if it exists) over the presentation used by evaluation campaigns.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF