We introduce a large dataset of narrative texts and questions about these texts, intended to be used in a machine comprehension task that requires reasoning using commonsense knowledge. Our dataset complements similar datasets in that we focus on stories about everyday activities, such as going to the movies or working in the garden, and that the questions require commonsense knowledge, or more specifically, script knowledge, to be answered. We show that our mode of data collection via crowdsourcing results in a substantial amount of such inference questions. The dataset forms the basis of a shared task on commonsense and script knowledge organized at SemEval 2018 and provides challenging test cases for the broader natural language understanding community.
Two notable exceptions are the and datasets. @cite_2 is a dataset of newswire texts from CNN with questions and answers written by crowdsourcing workers. closely resembles our own data collection with respect to the method of data acquisition. As for our data collection, full texts were not shown to workers as a basis for question formulation, but only the text's title and a short summary, to avoid literal repetitions and support the generation of non-trivial questions requiring background knowledge. The NewsQA text collection differs from ours in domain and genre (newswire texts vs. narrative stories about everyday events). Knowledge required to answer the questions is mostly factual knowledge and script knowledge is only marginally relevant. Also, the task is not exactly question answering, but identification of document passages containing the answer.
@cite_2
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One of the major challenges for automated question answering over Knowledge Bases (KBQA) is translating a natural language question to the Knowledge Base (KB) entities and predicates. Previous systems have used a limited amount of training data to learn a lexicon that is later used for question answering. This approach does not make use of other potentially relevant text data, outside the KB, which could supplement the available information. We introduce a new system, Text2KB, that enriches question answering over a knowledge base by using external text data. Specifically, we revisit different phases in the KBQA process and demonstrate that text resources improve question interpretation, candidate generation and ranking. Building on a state-of-the-art traditional KBQA system, Text2KB utilizes web search results, community question answering and general text document collection data, to detect question topic entities, map question phrases to KB predicates, and to enrich the features of the candidates derived from the KB. Text2KB significantly improves performance over the baseline KBQA method, as measured on a popular WebQuestions dataset. The results and insights developed in this work can guide future efforts on combining textual and structured KB data for question answering. We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. We collect this dataset through a four-stage process designed to solicit exploratory questions that require reasoning. A thorough analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (0.198 in F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available at this https URL. Much of what we understand from text is not explicitly stated. Rather, the reader uses his her knowledge to fill in gaps and create a coherent, mental picture or “scene” depicting what text appears to convey. The scene constitutes an understanding of the text, and can be used to answer questions that go beyond the text. Our goal is to answer elementary science questions, where this requirement is pervasive; A question will often give a partial description of a scene and ask the student about implicit information. We show that by using a simple “knowledge graph” representation of the question, we can leverage several large-scale linguistic resources to provide missing background knowledge, somewhat alleviating the knowledge bottleneck in previous approaches. The coherence of the best resulting scene, built from a question answer-candidate pair, reflects the confidence that the answer candidate is correct, and thus can be used to answer multiple choice questions. Our experiments show that this approach outperforms competitive algorithms on several datasets tested. The significance of this work is thus to show that a simple “knowledge graph” representation allows a version of “interpretation as scene construction” to be made viable. Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In this paper, we focus on the recently introduced ARC Challenge dataset, which contains 2,590 multiple choice questions authored for grade-school science exams. These questions are selected to be the most challenging for current QA systems, and current state of the art performance is only slightly better than random chance. We present a system that rewrites a given question into queries that are used to retrieve supporting text from a large corpus of science-related text. Our rewriter is able to incorporate background knowledge from ConceptNet and -- in tandem with a generic textual entailment system trained on SciTail that identifies support in the retrieved results -- outperforms several strong baselines on the end-to-end QA task despite only being trained to identify essential terms in the original source question. We use a generalizable decision methodology over the retrieved evidence and answer candidates to select the best answer. By combining query rewriting, background knowledge, and textual entailment our system is able to outperform several strong baselines on the ARC dataset. Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering. However, to tackle real-life question answering problems on multimedia collections such as personal photos, we have to look at whole collections with sequences of photos or videos. When answering questions from a large collection, a natural problem is to identify snippets to support the answer. In this paper, we describe a novel neural network called Focal Visual-Text Attention network (FVTA) for collective reasoning in visual question answering, where both visual and text sequence information such as images and text metadata are presented. FVTA introduces an end-to-end approach that makes use of a hierarchical process to dynamically determine what media and what time to focus on in the sequential data to answer the question. FVTA can not only answer the questions well but also provides the justifications which the system results are based upon to get the answers. FVTA achieves state-of-the-art performance on the MemexQA dataset and competitive results on the MovieQA dataset. Question Answering (QA) is retrieving answers to natural language questions from a collection of documents rather than retrieving relevant documents containing the keywords of the query which is performed by search engines. What a user usually wants is often a precise answer to a question. For example, given the question “Who won the nobel prize in peace in 2006?” what a user really wants is the answer “Dr. Muhammad Yunus”, in stead of reading through lots of documents that contain the words “win”, “nobel”,“prize”, “peace” and “2006” etc. This means that question answering systems will possibly be integral to the next generation of search engines. The Text Retrieval Conference, TREC1 QA track is the major large-scale evaluation environment for open-domain question answering systems. The questions in the TREC-2007 QA track are clustered by target, which is the overall theme or topic of the questions. The track has three types of questions: 1. factoid that require only one correct response, 2. list that require a non redundant list of correct responses and 3. other questions that require a non redundant list of facts about the target that has not already been discovered by a previous answer. We took the approach of designing a question answering system that is based on document tagging and question classification. Question classification extracts useful information (i.e. answer type) from the question about how to answer the question. Document tagging extracts useful information from the documents, which will be used in finding the answer to the question. We used different available tools to tag the documents. Our system classifies the questions using manually developed rules. This paper describes the Ephyra question answering engine, a modular and extensible framework that allows to integrate multiple approaches to question answering in one system Our framework can be adapted to languages other than English by replacing language-specific components It supports the two major approaches to question answering, knowledge annotation and knowledge mining Ephyra uses the web as a data resource, but could also work with smaller corpora In addition, we propose a novel approach to question interpretation which abstracts from the original formulation of the question Text patterns are used to interpret a question and to extract answers from text snippets Our system automatically learns the patterns for answer extraction, using question-answer pairs as training data Experimental results revealed the potential of this approach. Visual Question Answering (VQA) has attracted a lot of attention in both Computer Vision and Natural Language Processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the question and image alone. The set of such questions that require no external information to answer is interesting, but very limited. It excludes questions which require common sense, or basic factual knowledge to answer, for example. Here we introduce FVQA, a VQA dataset which requires, and supports, much deeper reasoning. FVQA only contains questions which require external information to answer. We thus extend a conventional visual question answering dataset, which contains image-question-answerg triplets, through additional image-question-answer-supporting fact tuples. The supporting fact is represented as a structural triplet, such as . We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting facts. We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text. The dataset consists of 14,944 questions about 408 movies with high semantic diversity. The questions range from simpler "Who" did "What" to "Whom", to "Why" and "How" certain events occurred. Each question comes with a set of five possible answers; a correct one and four deceiving answers provided by human annotators. Our dataset is unique in that it contains multiple sources of information -- video clips, plots, subtitles, scripts, and DVS. We analyze our data through various statistics and methods. We further extend existing QA techniques to show that question-answering with such open-ended semantics is hard. We make this data set public along with an evaluation benchmark to encourage inspiring work in this challenging domain. This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article. This task of machine reading at scale combines the challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer spans from those articles). Our approach combines a search component based on bigram hashing and TF-IDF matching with a multi-layer recurrent neural network model trained to detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA datasets indicate that (1) both modules are highly competitive with respect to existing counterparts and (2) multitask learning using distant supervision on their combination is an effective complete system on this challenging task. We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text. The dataset consists of 14,944 questions about 408 movies with high semantic diversity. The questions range from simpler "Who" did "What" to "Whom", to "Why" and "How" certain events occurred. Each question comes with a set of five possible answers, a correct one and four deceiving answers provided by human annotators. Our dataset is unique in that it contains multiple sources of information – video clips, plots, subtitles, scripts, and DVS [32]. We analyze our data through various statistics and methods. We further extend existing QA techniques to show that question-answering with such open-ended semantics is hard. We make this data set public along with an evaluation benchmark to encourage inspiring work in this challenging domain. In this paper we give an overview of the Knowledge Base Population (KBP) track at the 2010 Text Analysis Conference. The main goal of KBP is to promote research in discovering facts about entities and augmenting a knowledge base (KB) with these facts. This is done through two tasks, Entity Linking -- linking names in context to entities in the KB -- and Slot Filling -- adding information about an entity to the KB. A large source collection of newswire and web documents is provided from which systems are to discover information. Attributes ("slots") derived from Wikipedia infoboxes are used to create the reference KB. In this paper we provide an overview of the techniques which can serve as a basis for a good KBP system, lay out the remaining challenges by comparison with traditional Information Extraction (IE) and Question Answering (QA) tasks, and provide some suggestions to address these challenges. Answering science questions posed in natural language is an important AI challenge. Answering such questions often requires non-trivial inference and knowledge that goes beyond factoid retrieval. Yet, most systems for this task are based on relatively shallow Information Retrieval (IR) and statistical correlation techniques operating on large unstructured corpora. We propose a structured inference system for this task, formulated as an Integer Linear Program (ILP), that answers natural language questions using a semi-structured knowledge base derived from text, including questions requiring multi-step inference and a combination of multiple facts. On a dataset of real, unseen science questions, our system significantly outperforms (+14 ) the best previous attempt at structured reasoning for this task, which used Markov Logic Networks (MLNs). It also improves upon a previous ILP formulation by 17.7 . When combined with unstructured inference methods, the ILP system significantly boosts overall performance (+10 ). Finally, we show our approach is substantially more robust to a simple answer perturbation compared to statistical correlation methods. Retrieving topically-relevant text passages in documents has been studied many times, but finding non-factoid, multiple sentence answers to web queries is a different task that is becoming increasingly important for applications such as mobile search. As the first stage of developing retrieval models for "answer passages", we describe the process of creating a test collection of questions and multiple-sentence answers based on the TREC GOV2 queries and documents. This annotation shows that most of the description-length TREC queries do in fact have passage-level answers. We then examine the effectiveness of current passage retrieval models in terms of finding passages that contain answers. We show that the existing methods are not effective for this task, and also observe that the relative performance of these methods in retrieving answers does not correspond to their performance in retrieving relevant documents. We present two new large-scale datasets aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. The Quasar-S dataset consists of 37000 cloze-style (fill-in-the-gap) queries constructed from definitions of software entity tags on the popular website Stack Overflow. The posts and comments on the website serve as the background corpus for answering the cloze questions. The Quasar-T dataset consists of 43000 open-domain trivia questions and their answers obtained from various internet sources. ClueWeb09 serves as the background corpus for extracting these answers. We pose these datasets as a challenge for two related subtasks of factoid Question Answering: (1) searching for relevant pieces of text that include the correct answer to a query, and (2) reading the retrieved text to answer the query. We also describe a retrieval system for extracting relevant sentences and documents from the corpus given a query, and include these in the release for researchers wishing to only focus on (2). We evaluate several baselines on both datasets, ranging from simple heuristics to powerful neural models, and show that these lag behind human performance by 16.4 and 32.1 for Quasar-S and -T respectively. The datasets are available at this https URL . Visual Question Answering (VQA) has attracted much attention in both computer vision and natural language processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the question and image alone. The set of such questions that require no external information to answer is interesting, but very limited. It excludes questions which require common sense, or basic factual knowledge to answer, for example. Here we introduce FVQA (Fact-based VQA), a VQA dataset which requires, and supports, much deeper reasoning. FVQA primarily contains questions that require external information to answer. We thus extend a conventional visual question answering dataset, which contains image-question-answer triplets, through additional image-question-answer-supporting fact tuples. Each supporting-fact is represented as a structural triplet, such as . We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting-facts. Community-based Question Answering (CQA) has become popular in knowledge sharing sites since it allows users to get answers to complex, detailed, and personal questions directly from other users. Large archives of historical questions and associated answers have been accumulated. Retrieving relevant historical answers that best match a question is an essential component of a CQA service. Most state of the art approaches are based on bag-of-words models, which have been proven successful in a range of text matching tasks, but are insufficient for capturing the important word sequence information in short text matching. In this paper, a new architecture is proposed to more effectively model the complicated matching relations between questions and answers. It utilises a similarity matrix which contains both lexical and sequential information. Afterwards the information is put into a deep architecture to find potentially suitable answers. The experimental study shows its potential in improving matching accuracy of question and answer. Question Answering (QA) is a specific type of information retrieval. Given a set of documents, a Question Answering system attempts to find out the correct answer to the question pose in natural language. Question answering is multidisciplinary. It involves information technology, artificial intelligence, natural language processing, knowledge and database management and cognitive science. From the technological perspective, question answering uses natural or statistical language processing, information retrieval, and knowledge representation and reasoning as potential building blocks. It involves text classification, information extraction and summarization technologies. In general, question answering system (QAS) has three components such as question classification, information retrieval, and answer extraction. These components play a essential role in QAS. Question classification play primary role in QA system to categorize the question based upon on the type of its entity. Information retrieval method is get of identify success by extracting out applicable answer post by their intelligent question answering system. Finally, answer extraction module is rising topics in the QAS where these systems are often requiring ranking and validating a candidate’s answer. Most of the Question Answering systems consists of three main modules: question processing, document processing and answer processing. Question processing module plays an important part in QA systems. If this module doesn't work correctly, it will make problems for other sections. Moreover answer processing module is an emerging topic in Question Answering, in which these systems are often required to rank and validate candidate answers. These techniques aiming at discovering the short and precise answers are often based on the semantic classification. QA systems give the ability to answer questions posed in natural language by extracting, from a repository of documents, fragments of documents that contain material relevant to the answer. Automatic summarization of open domain spoken dialogues is a new research area. This paper introduces the task, the challenges involved, and presents an approach to obtain automatic extract summaries for multi-party dialogues of four different genres, without any restriction on domain. We address the following issues which are intrinsic to spoken dialogue summarization and typically can be ignored when summarizing written text such as newswire data: (i) detection and removal of speech disfluencies; (ii) detection and insertion of sentence boundaries; (iii) detection and linking of cross-speaker information units (question-answer pairs). A global system evaluation using a corpus of 23 relevance annotated dialogues containing 80 topical segments shows that for the two more informal genres, our summarization system using dialogue specific components significantly outperforms a baseline using TFIDF term weighting with maximum marginal relevance ranking (MMR). We present a method that learns to answer visual questions by selecting image regions relevant to the text-based query. Our method exhibits significant improvements in answering questions such as "what color," where it is necessary to evaluate a specific location, and "what room," where it selectively identifies informative image regions. Our model is tested on the VQA dataset which is the largest human-annotated visual question answering dataset to our knowledge.