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Semantic Parsing on Freebase from Question-Answer Pairs
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 fromExpand
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Semantic Parsing via Paraphrasing
A central challenge in semantic parsing is handling the myriad ways in which knowledge base predicates can be expressed. Traditionally, semantic parsers are trained primarily from text paired withExpand
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CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
When answering a question, people often draw upon their rich world knowledge in addition to some task-specific context. Recent work has focused primarily on answering questions based on some relevantExpand
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Building a Semantic Parser Overnight
How do we build a semantic parser in a new domain starting with zero training examples? We introduce a new methodology for this setting: First, we use a simple grammar to generate logical formsExpand
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Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
Extending the success of deep neural networks to natural language understanding and symbolic reasoning requires complex operations and external memory. Recent neural program induction approaches haveExpand
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The Web as a Knowledge-base for Answering Complex Questions
Answering complex questions is a time-consuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made headway in answering simpleExpand
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Global Learning of Typed Entailment Rules
Extensive knowledge bases of entailment rules between predicates are crucial for applied semantic inference. In this paper we propose an algorithm that utilizes transitivity constraints to learn aExpand
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Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
This paper presents MAPO: a novel policy optimization formulation that incorporates a memory buffer of promising trajectories to reduce the variance of policy gradient estimates for deterministicExpand
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Modeling Biological Processes for Reading Comprehension
Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this paper, we focus on a new readingExpand
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Text Segmentation as a Supervised Learning Task
Text segmentation, the task of dividing a document into contiguous segments based on its semantic structure, is a longstanding challenge in language understanding. Previous work on text segmentationExpand
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