ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters

To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as compositionality, temporal reasoning, and comparisons. ComQA questions come from the WikiAnswers community QA platform, which typically contains questions that are not satisfactorily answerable by existing search engine technology. Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers. ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters. We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing. We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA.Comment: 11 pages, NAACL 201

Open-domain web-based multiple document : question answering for list questions with support for temporal restrictors

Tese de doutoramento, Informática (Ciências da Computação), Universidade de Lisboa, Faculdade de Ciências, 2015With the growth of the Internet, more people are searching for information on the Web. The combination of web growth and improvements in Information Technology has reignited the interest in Question Answering (QA) systems. QA is a type of information retrieval combined with natural language processing techniques that aims at finding answers to natural language questions. List questions have been widely studied in the QA field. These are questions that require a list of correct answers, making the task of correctly answering them more complex. In List questions, the answers may lie in the same document or spread over multiple documents. In the latter case, a QA system able to answer List questions has to deal with the fusion of partial answers. The current Question Answering state-of-the-art does not provide yet a good way to tackle this complex problem of collecting the exact answers from multiple documents. Our goal is to provide better QA solutions to users, who desire direct answers, using approaches that deal with the complex problem of extracting answers found spread over several documents. The present dissertation address the problem of answering Open-domain List questions by exploring redundancy and combining it with heuristics to improve QA accuracy. Our approach uses the Web as information source, since it is several orders of magnitude larger than other document collections. Besides handling List questions, we develop an approach with special focus on questions that include temporal information. In this regard, the current work addresses a topic that was lacking specific research. A additional purpose of this dissertation is to report on important results of the research combining Web-based QA, List QA and Temporal QA. Besides the evaluation of our approach itself we compare our system with other QA systems in order to assess its performance relative to the state-of-the-art. Finally, our approaches to answer List questions and List questions with temporal information are implemented into a fully-fledged Open-domain Web-based Question Answering System that provides answers retrieved from multiple documents.Com o crescimento da Internet cada vez mais pessoas buscam informações usando a Web. A combinação do crescimento da Internet com melhoramentos na Tecnologia da Informação traz como consequência o renovado interesse em Sistemas de Respostas a Perguntas (SRP). SRP combina técnicas de recuperação de informação com ferramentas de apoio à linguagem natural com o objetivo de encontrar respostas para perguntas em linguagem natural. Perguntas do tipo lista têm sido largamente estudadas nesta área. Neste tipo de perguntas é esperada uma lista de respostas corretas, o que torna a tarefa de responder a perguntas do tipo lista ainda mais complexa. As respostas para este tipo de pergunta podem ser encontradas num único documento ou espalhados em múltiplos documentos. No último caso, um SRP deve estar preparado para lidar com a fusão de respostas parciais. Os SRP atuais ainda não providenciam uma boa forma de lidar com este complexo problema de coletar respostas de múltiplos documentos. Nosso objetivo é prover melhores soluções para utilizadores que desejam buscar respostas diretas usando abordagens para extrair respostas de múltiplos documentos. Esta dissertação aborda o problema de responder a perguntas de domínio aberto explorando redundância combinada com heurísticas. Nossa abordagem usa a Internet como fonte de informação uma vez que a Web é a maior coleção de documentos da atualidade. Para além de responder a perguntas do tipo lista, nós desenvolvemos uma abordagem para responder a perguntas com restrição temporal. Neste sentido, o presente trabalho aborda este tema onde há pouca investigação específica. Adicionalmente, esta dissertação tem o propósito de informar sobre resultados importantes desta pesquisa que combina várias áreas: SRP com base na Web, SRP especialmente desenvolvidos para responder perguntas do tipo lista e também com restrição temporal. Além da avaliação da nossa própria abordagem, comparamos o nosso sistema com outros SRP, a fim de avaliar o seu desempenho em relação ao estado da arte. Por fim, as nossas abordagens para responder a perguntas do tipo lista e perguntas do tipo lista com informações temporais são implementadas em um Sistema online de Respostas a Perguntas de domínio aberto que funciona diretamente sob a Web e que fornece respostas extraídas de múltiplos documentos.Fundação para a Ciência e a Tecnologia (FCT), SFRH/BD/65647/2009; European Commission, projeto QTLeap (Quality Translation by Deep Language Engineering Approache

Are Passages Enough? The MIRACLE Team Participation at QA@CLEF2009

Preceedins of: 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009.Took place September 30 - October 2, 2009,in Corfu, Greece. The event Web site is http://www.clef-campaign.org/2009.htmlThis paper summarizes the participation of the MIRACLE team in the Multilingual Question Answering Track at CLEF 2009. In this campaign, we took part in the monolingual Spanish task at ResPubliQA and submitted two runs. We have adapted our QA system to the new JRC-Acquis collection and the legal domain. We tested the use of answer filtering and ranking techniques against a baseline system using passage retrieval with no success. The run using question analysis and passage retrieval obtained a global accuracy of 0.33, while the addition of an answer filtering resulted in 0.29. We provide an analy-sis of the results for different questions types to investigate why it is difficult to leverage previous QA techniques. Another task of our work has been the appli-cation of temporal management to QA. Finally we include some discussion of the problems found with the new collection and the complexities of the domain.This work has been partially supported by the Research Network MAVIR (S-0505/TIC/000267) and by the project BRAVO (TIN2007- 67407-C3-01).Publicad

Question Generation for French: Collating Parsers and Paraphrasing Questions

This article describes a question generation system for French. The transformation of declarative sentences into questions relies on two different syntactic parsers and named entity recognition tools. This makes it possible to further diversify the questions generated and to possibly alleviate the problems inherent to the analysis tools. The system also generates reformulations for the questions based on variations in the question words, inducing answers with different granularities, and nominalisations of action verbs. We evaluate the questions generated for sentences extracted from two different corpora: a corpus of newspaper articles used for the CLEF Question Answering evaluation campaign and a corpus of simplified online encyclopedia articles. The evaluation shows that the system is able to generate a majority of good and medium quality questions. We also present an original evaluation of the question generation system using the question analysis module of a question answering system