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.
In this paper, we train a semantic parser that scales up to Freebase. Instead of relying on annotated logical forms, which is especially expensive to obtain at large scale, we learn from question-answer pairs. The main challenge in this setting is narrowing down the huge number of possible logical predicates for a given question. We tackle this problem in two ways: First, we build a coarse mapping from phrases to predicates using a knowledge base and a large text corpus. Second, we use a bridging operation to generate additional predicates based on neighboring predicates. On the dataset of Cai and Yates (2013), despite not having annotated logical forms, our system outperforms their state-of-the-art parser. Additionally, we collected a more realistic and challenging dataset of question-answer pairs and improves over a natural baseline. A method for preparing silica-containing olefin polymerization catalysts, and the process performable therewith, the preparation involving adding an alkali met al silicate to an acid under defined conditions of addition to produce a hydrogel, recovering the gel in the substantially dry condition by employment of an oxygenated organic compound and impregnating the gel with a chromium compound. 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.