PCFG Learning by Nonterminal Partition Search

Belz, Anja

doi:10.1007/3-540-45790-9_2

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…The problem of inferring a probabilistic context free grammar (PCFG) from a set of examples has been studied extensively for the purpose of natural language processing. Belz [15] extends standard split/merge grammar inference techniques to optimize grammars from examples, but requires a large corpus of annotated examples, which are not viable for inferring program input grammars. Scicluna and Higuera [35] propose an unsupervised approach to grammar inference without annotated examples.…”

Section: Related Workmentioning

confidence: 99%

REINAM: reinforcement learning for input-grammar inference

Johnson

Yang³

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Program input grammars (i.e., grammars encoding the language of valid program inputs) facilitate a wide range of applications in software engineering such as symbolic execution and delta debugging. Grammars synthesized by existing approaches can cover only a small part of the valid input space mainly due to unanalyzable code (e.g., native code) in programs and lacking high-quality and high-variety seed inputs. To address these challenges, we present REINAM, a reinforcement-learning approach for synthesizing probabilistic context-free program input grammars without any seed inputs. REINAM uses an industrial symbolic execution engine to generate an initial set of inputs for the given target program, and then uses an iterative process of grammar generalization to proactively generate additional inputs to infer grammars generalized from these initial seed inputs. To efficiently search for target generalizations in a huge search space of candidate generalization operators, REINAM includes a novel formulation of the search problem as a reinforcement learning problem. Our evaluation on 11 real-world benchmarks shows that REINAM outperforms an existing state-of-the-art approach on precision and recall of synthesized grammars, and fuzz testing based on REINAM substantially increases the coverage of the space of valid inputs. REINAM is able to synthesize a grammar covering the entire valid input space for some benchmarks without decreasing the accuracy of the grammar.

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Section: Related Workmentioning

confidence: 99%

REINAM: reinforcement learning for input-grammar inference

Johnson

Yang³

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Learning grammars for different parsing tasks by partition search

Belz

2002

Proceedings of the 19th International Conference on Computational Linguistics -

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This paper describes a comparative application of Grammar Learning by Partition Search to four different learning tasks: deep parsing, NP identification, flat phrase chunking and NP chunking. In the experiments, base grammars were extracted from a treebank corpus. From this starting point, new grammars optimised for the different parsing tasks were learnt by Partition Search. No lexical information was used. In half of the experiments, local structural context in the form of parent phrase category information was incorporated into the grammars. Results show that grammars which contain this information outperform grammars which do not by large margins in all tests for all parsing tasks. It makes the biggest difference for deep parsing, typically corresponding to an improvement of around 5%. Overall, Partition Search with parent phrase category information is shown to be a successful method for learning grammars optimised for a given parsing task, and for minimising grammar size. The biggest margin of improvement over a base grammar was a 5.4% increase in the F-Score for deep parsing. The biggest size reductions were 93.5% fewer nonterminals (for NP identification), and 31.3% fewer rules (for XP chunking

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PCFG Learning by Nonterminal Partition Search

Cited by 2 publications

References 6 publications

REINAM: reinforcement learning for input-grammar inference

REINAM: reinforcement learning for input-grammar inference

Learning grammars for different parsing tasks by partition search

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