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How Good Are My Predictions? Efficiently Approximating Precision-Recall Curves for Massive Datasets
Ashish Sabharwal and Hanie SedghiUAI • 2017 Large scale machine learning produces massive datasets whose items are often associated with a confidence level and can thus be ranked. However, computing the precision of these resources requires human annotation, which is often prohibitively expensive and…Learning What is Essential in Questions
Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Dan RothCoNLL • 2017 Question answering (QA) systems are easily distracted by irrelevant or redundant words in questions, especially when faced with long or multi-sentence questions in difficult domains. This paper introduces and studies the notion of essential question terms…Leveraging Term Banks for Answering Complex Questions: A Case for Sparse Vectors
Peter D. TurneyarXiv • 2017 While open-domain question answering (QA) systems have proven effective for answering simple questions, they struggle with more complex questions. Our goal is to answer more complex questions reliably, without incurring a significant cost in knowledge…Neural Semantic Parsing with Type Constraints for Semi-Structured Tables
Jayant Krishnamurthy, Pradeep Dasigi, and Matt GardnerEMNLP • 2017 We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations: (1) a grammar for the decoder that only generates well-typed…QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations
Nan-Chen Chen and Been KimVAST • 2017 Developing sophisticated artificial intelligence (AI) systems requires AI researchers to experiment with different designs and analyze results from evaluations (we refer this task as evaluation analysis). In this paper, we tackle the challenges of evaluation…Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification
Rebecca Sharp, Mihai Surdeanu, Peter Jansen, Marco A. Valenzuela-Escárcega, Peter Clark, and Michael HammondCoNLL • 2017 For many applications of question answering (QA), being able to explain why a given model chose an answer is critical. However, the lack of labeled data for answer justifications makes learning this difficult and expensive. Here we propose an approach that…The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task
Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, Noah A. SmithCoNLL • 2017 A writer’s style depends not just on personal traits but also on her intent and mental state. In this paper, we show how variants of the same writing task can lead to measurable differences in writing style. We present a case study based on the story cloze…Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge
Matt Gardner and Jayant KrishnamurthyAAAI • 2017 Traditional semantic parsers map language onto compositional, executable queries in a fixed schema. This map- ping allows them to effectively leverage the information con- tained in large, formal knowledge bases (KBs, e.g., Freebase) to answer questions, but…Probabilistic Neural Programs
Kenton W. Murray and Jayant KrishnamurthyNIPS • NAMPI Workshop • 2016 We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultaneously enabling the use of deep neural networks. Probabilistic neural programs…Tables as Semi-structured Knowledge for Question Answering
Sujay Kumar Jauhar, Peter D. Turney, Eduard HovyACL • 2016 Question answering requires access to a knowledge base to check facts and reason about information. Knowledge in the form of natural language text is easy to acquire, but difficult for automated reasoning. Highly-structured knowledge bases can facilitate…