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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Designing computer systems to understand natural language input is a difficult task. In recent years there has been considerable interest in corpus-based methods for constructing natural language parsers. These empirical approaches replace hand-crafted grammars with linguistic models acquired through automated training over language corpora. A common thread among such methods to date is the use of propositional or probabilistic representations for the learned knowledge. This dissertation presents an alternative approach based on techniques from a subfield of machine learning known as inductive logic programming (ILP). ILP, which investigates the learning of relational (first-order) rules, provides an empirical method for acquiring knowledge within traditional symbolic parsing frameworks. This dissertation details the architecture, implementation and evaluation of C scHILL, a computer system for acquiring natural language parsers by training over corpora of parsed text. C scHILL treats language acquisition as the learning of search-control rules within a logic program that implements a shift-reduce parser. Control rules are induced using a novel ILP algorithm which handles difficult issues arising in the induction of search-control heuristics. Both the control-rule framework and the induction algorithm are crucial to C scHILL's success. The main advantage of C scHILL over propositional counterparts is its flexibility in handling varied representations. C scHILL has produced parsers for various analyses including case-role mapping, detailed syntactic parse trees, and a logical form suitable for expressing first-order database queries. All of these tasks are accomplished within the same framework, using a single, general learning method that can acquire new syntactic and semantic categories for resolving ambiguities. Experimental evidence from both artificial and real-world corpora demonstrates that C scHILL learns parsers as well or better than previous artificial neural network or probabilistic approaches on comparable tasks. In the database query domain, which goes beyond the scope of previous empirical approaches, the learned parser outperforms an existing hand-crafted system. These results support the claim that ILP techniques as implemented in C scHILL represent a viable alternative with significant potential advantages over neural-network, propositional, and probabilistic approaches to empirical parser construction. Semantic parsing aims at mapping natural language to machine interpretable meaning representations. Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domainor representation-specific. In this paper we present a general method based on an attention-enhanced encoder-decoder model. We encode input utterances into vector representations, and generate their logical forms by conditioning the output sequences or trees on the encoding vectors. Experimental results on four datasets show that our approach performs competitively without using hand-engineered features and is easy to adapt across domains and meaning representations. In this paper, we present an algorithm for learning a generative model of natural language sentences together with their formal meaning representations with hierarchical structures. The model is applied to the task of mapping sentences to hierarchical representations of their underlying meaning. We introduce dynamic programming techniques for efficient training and decoding. In experiments, we demonstrate that the model, when coupled with a discriminative reranking technique, achieves state-of-the-art performance when tested on two publicly available corpora. The generative model degrades robustly when presented with instances that are different from those seen in training. This allows a notable improvement in recall compared to previous models. We present a new approach for mapping natural language sentences to their formal meaning representations using string-kernel-based classifiers. Our system learns these classifiers for every production in the formal language grammar. Meaning representations for novel natural language sentences are obtained by finding the most probable semantic parse using these string classifiers. Our experiments on two real-world data sets show that this approach compares favorably to other existing systems and is particularly robust to noise. We present a novel statistical approach to semantic parsing, WASP, for constructing a complete, formal meaning representation of a sentence. A semantic parser is learned given a set of sentences annotated with their correct meaning representations. The main innovation of WASP is its use of state-of-the-art statistical machine translation techniques. A word alignment model is used for lexical acquisition, and the parsing model itself can be seen as a syntax-based translation model. We show that WASP performs favorably in terms of both accuracy and coverage compared to existing learning methods requiring similar amount of supervision, and shows better robustness to variations in task complexity and word order.