Semantic Parsing on Freebase from Question-Answer Pairs
Jonathan Berant low, Andrew Chou low, Roy Frostig, Percy Liang
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.
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| Topic Modeling | Computer Science |
| Natural Language Processing Techniques | Computer Science |
| Multimodal Machine Learning Applications | Computer Science |
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