We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to produce well calibrated confidence scores for their results on individual paragraphs. We sample multiple paragraphs from the documents during training, and use a shared-normalization training objective that encourages the model to produce globally correct output. We combine this method with a state-of-the-art pipeline for training models on document QA data. Experiments demonstrate strong performance on several document QA datasets. Overall, we are able to achieve a score of 71.3 F1 on the web portion of TriviaQA, a large improvement from the 56.7 F1 of the previous best system.
Open question answering has been the subject of much research, especially spurred by the TREC question answering track @cite_15 . Knowledge bases can be used, such as in @cite_0 , although the resulting systems are limited by the quality of the knowledge base. Systems that try to answer questions using natural language resources such as YodaQA @cite_17 typically use pipelined methods to retrieve related text, build answer candidates, and pick a final output.
@cite_0 @cite_15 @cite_17
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We briefly survey the current state of art in the field of Question Answering and present the YodaQA system, an open source framework for this task and a baseline pipeline with reasonable performance. We take a holistic approach, reviewing and aiming to integrate many different question answering task definitions and approaches concerning classes of knowledge bases, question representation and answer generation. To ease performance comparisons of general-purpose QA systems, we also propose an effort in building a new reference QA testing corpus which is a curated and extended version of the TREC corpus. 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 consider the problem of open-domain question answering (Open QA) over massive knowledge bases (KBs). Existing approaches use either manually curated KBs like Freebase or KBs automatically extracted from unstructured text. In this paper, we present OQA, the first approach to leverage both curated and extracted KBs. A key technical challenge is designing systems that are robust to the high variability in both natural language questions and massive KBs. OQA achieves robustness by decomposing the full Open QA problem into smaller sub-problems including question paraphrasing and query reformulation. OQA solves these sub-problems by mining millions of rules from an unlabeled question corpus and across multiple KBs. OQA then learns to integrate these rules by performing discriminative training on question-answer pairs using a latent-variable structured perceptron algorithm. We evaluate OQA on three benchmark question sets and demonstrate that it achieves up to twice the precision and recall of a state-of-the-art Open QA system. 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.