This article describes a neural semantic parser that maps natural language utterances onto logical forms that can be executed against a task-specific environment, such as a knowledge base or a data...
Various types of supervision have been explored to train semantic parsers, ranging from full supervision with utterance-logical form pairs to unsupervised semantic parsing without given utterances. Early work of statistical semantic parsing has mostly used annotated training data consisting of utterances paired with logical forms @cite_35 @cite_50 @cite_1 @cite_58 @cite_0 @cite_72 . Same applies to some of the recent work on neural semantic parsing @cite_33 @cite_65 . This form of supervision is the most effective to train the parser, but is also expensive to obtain. In order to write down a correct logical form, the annotator not only needs to have expertise in the semantic formalism, but also has to ensure the logical form matches the utterance semantics and contains no grammatical mistakes. For this reason, fully supervised training applies more to small, close domain problems, such as querying the US geographical database @cite_35 .
@cite_35 @cite_33 @cite_1 @cite_65 @cite_0 @cite_72 @cite_50 @cite_58
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We present a method for training a semantic parser using only a knowledge base and an unlabeled text corpus, without any individually annotated sentences. Our key observation is that multiple forms of weak supervision can be combined to train an accurate semantic parser: semantic supervision from a knowledge base, and syntactic supervision from dependency-parsed sentences. We apply our approach to train a semantic parser that uses 77 relations from Freebase in its knowledge representation. This semantic parser extracts instances of binary relations with state-of-the-art accuracy, while simultaneously recovering much richer semantic structures, such as conjunctions of multiple relations with partially shared arguments. We demonstrate recovery of this richer structure by extracting logical forms from natural language queries against Freebase. On this task, the trained semantic parser achieves 80 precision and 56 recall, despite never having seen an annotated logical form. This paper presents a method for learning a semantic parser from ambiguous supervision. Training data consists of natural language sentences annotated with multiple potential meaning representations, only one of which is correct. Such ambiguous supervision models the type of supervision that can be more naturally available to language-learning systems. Given such weak supervision, our approach produces a semantic parser that maps sentences into meaning representations. An existing semantic parsing learning system that can only learn from unambiguous supervision is augmented to handle ambiguous supervision. Experimental results show that the resulting system is able to cope up with ambiguities and learn accurate semantic parsers. Supervised training procedures for semantic parsers produce high-quality semantic parsers, but they have difficulty scaling to large databases because of the sheer number of logical constants for which they must see labeled training data. We present a technique for developing semantic parsers for large databases based on a reduction to standard supervised training algorithms, schema matching, and pattern learning. Leveraging techniques from each of these areas, we develop a semantic parser for Freebase that is capable of parsing questions with an F1 that improves by 0.42 over a purely-supervised learning algorithm. A fundamental challenge in developing semantic parsers is the paucity of strong supervision in the form of language utterances annotated with logical form. In this paper, we propose to exploit structural regularities in language in different domains, and train semantic parsers over multiple knowledge-bases (KBs), while sharing information across datasets. We find that we can substantially improve parsing accuracy by training a single sequence-to-sequence model over multiple KBs, when providing an encoding of the domain at decoding time. Our model achieves state-of-the-art performance on the Overnight dataset (containing eight domains), improves performance over a single KB baseline from 75.6 to 79.6 , while obtaining a 7x reduction in the number of model parameters.