An Efficient Implementation of the Head-Corner Parser

This paper describes an efficient and robust implementation of a bi-directional, head-driven parser for constraint-based grammars. This parser is developed for the OVIS system: a Dutch spoken dialogue system in which information about public transport can be obtained by telephone. After a review of the motivation for head-driven parsing strategies, and head-corner parsing in particular, a non-deterministic version of the head-corner parser is presented. A memoization technique is applied to obtain a fast parser. A goal-weakening technique is introduced which greatly improves average case efficiency, both in terms of speed and space requirements. I argue in favor of such a memoization strategy with goal-weakening in comparison with ordinary chart-parsers because such a strategy can be applied selectively and therefore enormously reduces the space requirements of the parser, while no practical loss in time-efficiency is observed. On the contrary, experiments are described in which head-corner and left-corner parsers implemented with selective memoization and goal weakening outperform `standard' chart parsers. The experiments include the grammar of the OVIS system and the Alvey NL Tools grammar. Head-corner parsing is a mix of bottom-up and top-down processing. Certain approaches towards robust parsing require purely bottom-up processing. Therefore, it seems that head-corner parsing is unsuitable for such robust parsing techniques. However, it is shown how underspecification (which arises very naturally in a logic programming environment) can be used in the head-corner parser to allow such robust parsing techniques. A particular robust parsing model is described which is implemented in OVIS.Comment: 31 pages, uses cl.st

An Efficient Distribution of Labor in a Two Stage Robust Interpretation Process

Although Minimum Distance Parsing (MDP) offers a theoretically attractive solution to the problem of extragrammaticality, it is often computationally infeasible in large scale practical applications. In this paper we present an alternative approach where the labor is distributed between a more restrictive partial parser and a repair module. Though two stage approaches have grown in popularity in recent years because of their efficiency, they have done so at the cost of requiring hand coded repair heuristics. In contrast, our two stage approach does not require any hand coded knowledge sources dedicated to repair, thus making it possible to achieve a similar run time advantage over MDP without losing the quality of domain independence.Comment: 9 pages, 1 Postscript figure, uses aclap.sty and psfig.tex, In Proceedings of EMNLP 199

The Mystro system: A comprehensive translator toolkit

Mystro is a system that facilities the construction of compilers, assemblers, code generators, query interpretors, and similar programs. It provides features to encourage the use of iterative enhancement. Mystro was developed in response to the needs of NASA Langley Research Center (LaRC) and enjoys a number of advantages over similar systems. There are other programs available that can be used in building translators. These typically build parser tables, usually supply the source of a parser and parts of a lexical analyzer, but provide little or no aid for code generation. In general, only the front end of the compiler is addressed. Mystro, on the other hand, emphasizes tools for both ends of a compiler

A Syntactic Neural Model for General-Purpose Code Generation

We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python. Existing data-driven methods treat this problem as a language generation task without considering the underlying syntax of the target programming language. Informed by previous work in semantic parsing, in this paper we propose a novel neural architecture powered by a grammar model to explicitly capture the target syntax as prior knowledge. Experiments find this an effective way to scale up to generation of complex programs from natural language descriptions, achieving state-of-the-art results that well outperform previous code generation and semantic parsing approaches.Comment: To appear in ACL 201