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.
There are a number of highly developed full pipeline QA approaches using either the Web, as does QuASE @cite_30 , or Wikipedia as a resource, as do Microsoft's AskMSR @cite_2 , IBM's DeepQA @cite_13 and YodaQA @cite_17 @cite_4 --- the latter of which is open source and hence reproducible for comparison purposes. AskMSR is a search-engine based QA system that relies on data redundancy rather than sophisticated linguistic analyses of either questions or candidate answers'', i.e., it does not focus on machine comprehension, as we do. DeepQA is a very sophisticated system that relies on both unstructured information including text documents as well as structured data such as KBs, databases and ontologies to generate candidate answers or vote over evidence. YodaQA is an open source system modeled after DeepQA, similarly combining websites, information extraction, databases and Wikipedia in particular. Our comprehension task is made more challenging by only using a single resource. Comparing against these methods provides a useful datapoint for an upper bound'' benchmark on performance.
@cite_30 @cite_4 @cite_2 @cite_13 @cite_17
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This is a preprint, submitted on 2015-03-22. Question Answering as a sub-field of information retrieval and information extraction is recently enjoying renewed pop- ularity, triggered by the publicized success of IBM Watson in the Jeopardy! competition. But Question Answering re- search is now proceeding in several semi-independent tiers depending on the precise task formulation and constraints on the knowledge base, and new researchers entering the field can focus only on various restricted sub-tasks as no modern full-scale software system for QA has been openly available until recently. By our YodaQA system that we introduce here, we seek to re- unite and boost research efforts in Question Answering, pro- viding a modular, open source pipeline for this task — allow- ing integration of various knowledge base paradigms, an- swer production and analysis strategies and using a machine learned models to rank the answers. Within this pipeline, we also supply a baseline QA system inspired by DeepQA with solid performance and propose a reference experimen- tal setup for easy future performance comparisons. In this paper, we review the available open QA platforms, present the architecture of our pipeline, the components of the baseline QA system, and also analyze the system perfor- mance on the reference dataset. 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. 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. A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer. Our models have achieved state-of-the-art results on three public open-domain QA datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8 percentage points of improvement over the former two datasets.