On the voice-activated question answering

[EN] Question answering (QA) is probably one of the most challenging tasks in the field of natural language processing. It requires search engines that are capable of extracting concise, precise fragments of text that contain an answer to a question posed by the user. The incorporation of voice interfaces to the QA systems adds a more natural and very appealing perspective for these systems. This paper provides a comprehensive description of current state-of-the-art voice-activated QA systems. Finally, the scenarios that will emerge from the introduction of speech recognition in QA will be discussed. © 2006 IEEE.This work was supported in part by Research Projects TIN2009-13391-C04-03 and TIN2008-06856-C05-02. This paper was recommended by Associate Editor V. Marik.Rosso, P.; Hurtado Oliver, LF.; Segarra Soriano, E.; Sanchís Arnal, E. (2012). On the voice-activated question answering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 42(1):75-85. https://doi.org/10.1109/TSMCC.2010.2089620S758542

Finding answers to questions, in text collections or web, in open domain or specialty domains

International audienceThis chapter is dedicated to factual question answering, i.e. extracting precise and exact answers to question given in natural language from texts. A question in natural language gives more information than a bag of word query (i.e. a query made of a list of words), and provides clues for finding precise answers. We will first focus on the presentation of the underlying problems mainly due to the existence of linguistic variations between questions and their answerable pieces of texts for selecting relevant passages and extracting reliable answers. We will first present how to answer factual question in open domain. We will also present answering questions in specialty domain as it requires dealing with semi-structured knowledge and specialized terminologies, and can lead to different applications, as information management in corporations for example. Searching answers on the Web constitutes another application frame and introduces specificities linked to Web redundancy or collaborative usage. Besides, the Web is also multilingual, and a challenging problem consists in searching answers in target language documents other than the source language of the question. For all these topics, we present main approaches and the remaining problems

MegaWika: Millions of reports and their sources across 50 diverse languages

To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials. We process this dataset for a myriad of applications, going beyond the initial Wikipedia citation extraction and web scraping of content, including translating non-English articles for cross-lingual applications and providing FrameNet parses for automated semantic analysis. MegaWika is the largest resource for sentence-level report generation and the only report generation dataset that is multilingual. We manually analyze the quality of this resource through a semantically stratified sample. Finally, we provide baseline results and trained models for crucial steps in automated report generation: cross-lingual question answering and citation retrieval.Comment: Submitted to ACL, 202

Polyglot Semantic Parsing in APIs

Traditional approaches to semantic parsing (SP) work by training individual models for each available parallel dataset of text-meaning pairs. In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages. In particular, we focus on translating text to code signature representations using the software component datasets of Richardson and Kuhn (2017a,b). The advantage of such models is that they can be used for parsing a wide variety of input natural languages and output programming languages, or mixed input languages, using a single unified model. To facilitate modeling of this type, we develop a novel graph-based decoding framework that achieves state-of-the-art performance on the above datasets, and apply this method to two other benchmark SP tasks.Comment: accepted for NAACL-2018 (camera ready version

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

Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs

Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines

Information Retrieval: Recent Advances and Beyond

In this paper, we provide a detailed overview of the models used for information retrieval in the first and second stages of the typical processing chain. We discuss the current state-of-the-art models, including methods based on terms, semantic retrieval, and neural. Additionally, we delve into the key topics related to the learning process of these models. This way, this survey offers a comprehensive understanding of the field and is of interest for for researchers and practitioners entering/working in the information retrieval domain

Bootstrapping named entity resources for adaptive question answering systems

Los Sistemas de Búsqueda de Respuestas (SBR) amplían las capacidades de un buscador de información tradicional con la capacidad de encontrar respuestas precisas a las preguntas del usuario. El objetivo principal es facilitar el acceso a la información y disminuir el tiempo y el esfuerzo que el usuario debe emplear para encontrar una información concreta en una lista de documentos relevantes. En esta investigación se han abordado dos trabajos relacionados con los SBR. La primera parte presenta una arquitectura para SBR en castellano basada en la combinación y adaptación de diferentes técnicas de Recuperación y de Extracción de Información. Esta arquitectura está integrada por tres módulos principales que incluyen el análisis de la pregunta, la recuperación de pasajes relevantes y la extracción y selección de respuestas. En ella se ha prestado especial atención al tratamiento de las Entidades Nombradas puesto que, con frecuencia, son el tema de las preguntas o son buenas candidatas como respuestas. La propuesta se ha encarnado en el SBR del grupo MIRACLE que ha sido evaluado de forma independiente durante varias ediciones en la tarea compartida CLEF@QA, parte del foro de evaluación competitiva Cross-Language Evaluation Forum (CLEF). Se describen aquí las participaciones y los resultados obtenidos entre 2004 y 2007. El SBR de MIRACLE ha obtenido resultados moderados en el desempeño de la tarea con tasas de respuestas correctas entre el 20% y el 30%. Entre los resultados obtenidos destacan los de la tarea principal de 2005 y la tarea piloto de Búsqueda de Respuestas en tiempo real de 2006, RealTimeQA. Esta última tarea, además de requerir respuestas correctas incluía el tiempo de respuesta como un factor adicional en la evaluación. Estos resultados respaldan la validez de la arquitectura propuesta como una alternativa viable para los SBR sobre colecciones textuales y también corrobora resultados similares para el inglés y otras lenguas. Por otro lado, el análisis de los resultados a lo largo de las diferentes ediciones de CLEF así como la comparación con otros SBR apunta nuevos problemas y retos. Según nuestra experiencia, los sistemas de QA son más complicados de adaptar a otros dominios y lenguas que los sistemas de Recuperación de Información. Este problema viene heredado del uso de herramientas complejas de análisis de lenguaje como analizadores morfológicos, sintácticos y semánticos. Entre estos últimos se cuentan las herramientas para el Reconocimiento y Clasificación de Entidades Nombradas (NERC en inglés) así como para la Detección y Clasificación de Relaciones (RDC en inglés). Debido a la di cultad de adaptación del SBR a distintos dominios y colecciones, en la segunda parte de esta tesis se investiga una propuesta diferente basada en la adquisición de conocimiento mediante métodos de aprendizaje ligeramente supervisado. El objetivo de esta investigación es adquirir recursos semánticos útiles para las tareas de NERC y RDC usando colecciones de textos no anotados. Además, se trata de eliminar la dependencia de herramientas de análisis lingüístico con el fin de facilitar que las técnicas sean portables a diferentes dominios e idiomas. En primer lugar, se ha realizado un estudio de diferentes algoritmos para NERC y RDC de forma semisupervisada a partir de unos pocos ejemplos (bootstrapping). Este trabajo propone primero una arquitectura común y compara diferentes funciones que se han usado en la evaluación y selección de resultados intermedios, tanto instancias como patrones. La principal propuesta es un nuevo algoritmo que permite la adquisición simultánea e iterativa de instancias y patrones asociados a una relación. Incluye también la posibilidad de adquirir varias relaciones de forma simultánea y mediante el uso de la hipótesis de exclusividad obtener mejores resultados. Como característica distintiva el algoritmo explora la colección de textos con una estrategia basada en indización, que permite adquirir conocimiento de grandes colecciones. La estrategia de selección de candidatos y la evaluación se basan en la construcción de un grafo de instancias y patrones, que justifica nuestro método para la selección de candidatos. Este procedimiento es semejante al frente de exploración de una araña web y permite encontrar las instancias más parecidas a las semillas con las evidencias disponibles. Este algoritmo se ha implementado en el sistema SPINDEL y para su evaluación se ha comenzado con el caso concreto de la adquisición de recursos para las clases de Entidades Nombradas más comunes, Persona, Lugar y Organización. El objetivo es adquirir nombres asociados a cada una de las categorías así como patrones contextuales que permitan detectar menciones asociadas a una clase. Se presentan resultados para la adquisición de dos idiomas distintos, castellano e inglés, y para el castellano, en dos dominios diferentes, noticias y textos de una enciclopedia colaborativa, Wikipedia. En ambos casos el uso de herramientas de análisis lingüístico se ha limitado de acuerdo con el objetivo de avanzar hacia la independencia de idioma. Las listas adquiridas mediante bootstrapping parten de menos de 40 semillas por clase y obtienen del orden de 30.000 instancias de calidad variable. Además se obtienen listas de patrones indicativos asociados a cada clase de entidad. La evaluación indirecta confirma la utilidad de ambos recursos en la clasificación de Entidades Nombradas usando un enfoque simple basado únicamente en diccionarios. La mejor configuración obtiene para la clasificación en castellano una medida F de 67,17 y para inglés de 55,99. Además se confirma la utilidad de los patrones adquiridos que en ambos casos ayudan a mejorar la cobertura. El módulo requiere menor esfuerzo de desarrollo que los enfoques supervisados, si incluimos la necesidad de anotación, aunque su rendimiento es inferior por el momento. En definitiva, esta investigación constituye un primer paso hacia el desarrollo de aplicaciones semánticas como los SBR que requieran menos esfuerzo de adaptación a un dominio o lenguaje nuevo.-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Question Answering (QA) systems add new capabilities to traditional search engines with the ability to find precise answers to user questions. Their objective is to enable easier information access by reducing the time and effort that the user requires to find a concrete information among a list of relevant documents. In this thesis we have carried out two works related with QA systems. The first part introduces an architecture for QA systems for Spanish which is based on the combination and adaptation of different techniques from Information Retrieval (IR) and Information Extraction (IE). This architecture is composed by three modules that include question analysis, relevant passage retrieval and answer extraction and selection. The appropriate processing of Named Entities (NE) has received special attention because of their importance as question themes and candidate answers. The proposed architecture has been implemented as part of the MIRACLE QA system. This system has taken part in independent evaluations like the CLEF@QA track in the Cross-Language Evaluation Forum (CLEF). Results from 2004 to 2007 campaigns as well as the details and the evolution of the system have been described in deep. The MIRACLE QA system has obtained moderate performance with a first answer accuracy ranging between 20% and 30%. Nevertheless, it is important to highlight the results obtained in the 2005 main QA task and the RealTimeQA pilot task in 2006. The last one included response time as an important additional variable of the evaluation. These results back the proposed architecture as an option for QA from textual collection and confirm similar findings obtained for English and other languages. On the other hand, the analysis of the results along evaluation campaigns and the comparison with other QA systems point problems with current systems and new challenges. According to our experience, it is more dificult to tailor QA systems to different domains and languages than IR systems. The problem is inherited by the use of complex language analysis tools like POS taggers, parsers and other semantic analyzers, like NE Recognition and Classification (NERC) and Relation Detection and Characterization (RDC) tools. The second part of this thesis tackles this problem and proposes a different approach to adapting QA systems for di erent languages and collections. The proposal focuses on acquiring knowledge for the semantic analyzers based on lightly supervised approaches. The goal is to obtain useful resources that help to perform NERC or RDC using as few annotated resources as possible. Besides, we try to avoid dependencies from other language analysis tools with the purpose that these methods apply to different languages and domains. First of all, we have study previous work on building NERC and RDC modules with few supervision, particularly bootstrapping methods. We propose a common framework for different bootstrapping systems that help to unify different evaluation functions for intermediate results. The main proposal is a new algorithm that is able to simultaneously acquire instances and patterns associated to a relation of interest. It also uses mutual exclusion among relations to reduce concept drift and achieve better results. A distinctive characteristic is that it uses a query based exploration strategy of the text collection which enables their use for larger collections. Candidate selection and evaluation are based on incrementally building a graph of instances and patterns which also justifies our evaluation function. The discovery approach is analogous to the front of exploration in a web crawler and it is able to find the most similar instances to the available seeds. This algorithm has been implemented in the SPINDEL system. We have selected for evaluation the task of acquiring resources for the most common NE classes, Person, Location and Organization. The objective is to acquire name instances that belong to any of the classes as well as contextual patterns that help to detect mentions of NE that belong to that class. We present results for the acquisition of resources from raw text from two different languages, Spanish and English. We also performed experiments for Spanish in two different collections, news and texts from a collaborative encyclopedia, Wikipedia. Both cases are tackled with limited language analysis tools and resources. With an initial list of 40 instance seeds, the bootstrapping process is able to acquire large name lists containing up to 30.000 instances with a variable quality. Besides, large lists of indicative patterns are obtained too. Our indirect evaluation confirms the utility of both resources to classify NE using a simple dictionary recognition approach. Best results for Spanish obtained a F-score of 67,17 and for English this value is 55,99. The module requires much less development effort than annotation for supervised algorithms although the performance is not in pair yet. This research is a first step towards the development of semantic applications like QA for a new language or domain with no annotated corpora that requires less adaptation effort