Towards a Calculus of Object Programs

Meyer, Bertrand

doi:10.1007/978-1-4471-2350-7_8

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…The method presented here, a Calculus of Object Programs , combines four components: compositional logic , a framework for describing program semantics and proving program properties; negative variables to address the specifics of O-O programming , in particular qualified calls ;the alias calculus, which determines whether reference expressions can ever have the same value... Classes: software engineering (cs.se) , programming languages (cs.pl) , logic in computer science (cs.lo) Table 8: Highlighting tokens: two random paper abstracts in AAPD (Meyer, 2011). Dark color means a higher correlation between token and classes.…”

Section: Case Studymentioning

confidence: 99%

Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)

2022

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Generative commonsense reasoning (GCR) in natural language is to reason about the commonsense while generating coherent text. Recent years have seen a surge of interest in improving the generation quality of commonsense reasoning tasks. Nevertheless, these approaches have seldom investigated diversity in the GCR tasks, which aims to generate alternative explanations for a real-world situation or predict all possible outcomes. Diversifying GCR is challenging as it expects to generate multiple outputs that are not only semantically different but also grounded in commonsense knowledge. In this paper, we propose MoKGE, a novel method that diversifies the generative reasoning by a mixture of expert (MoE) strategy on commonsense knowledge graphs (KG). A set of knowledge experts seek diverse reasoning on KG to encourage various generation outputs. Empirical experiments demonstrated that MoKGE can significantly improve the diversity while achieving on par performance on accuracy on two GCR benchmarks, based on both automatic and human evaluations.

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Section: Case Studymentioning

confidence: 99%

Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)

2022

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Section: Case Studymentioning

confidence: 99%

LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification

Li¹,

Feng²,

Wu³

et al. 2022

Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)

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Multi-label text classification (MLTC) is an attractive and challenging task in natural language processing (NLP). Compared with single-label text classification, MLTC has a wider range of applications in practice. In this paper, we propose a label-interpretable graph convolutional network model to solve the MLTC problem by modeling tokens and labels as nodes in a heterogeneous graph. In this way, we are able to take into account multiple relationships including token-level relationships. Besides, the model allows better interpretability for predicted labels as the token-label edges are exposed. We evaluate our method on four real-world datasets and it achieves competitive scores against selected baseline methods. Specifically, this model achieves a gain of 0.14 on the F1 score in the small label set MLTC, and 0.07 in the large label set scenario.

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“…The notion was introduced in [11] and [12], in the context of developing the "alias calculus" for automatic may-alias analysis of OO programs; the calculus needs negative variables in the rules for qualified calls. The present work generalizes the original concept, showing that beyond alias analysis it can provide a framework for reasoning about a wide variety of properties of object-oriented programs.…”

Section: Negative Variable Basicsmentioning

confidence: 99%

“…Negative variables are not explicitly used in these approaches and are not available for formal reasoning on program properties. Meyer's "Calculus of Object Programs" [12] is an exception, integrating the alias calculus [11] and negative variables. Schoeller's path-based alias analysis [16] comes close to the need to use negative variables, but still uses the standard substitution technique to describe the semantics of a qualified method call.…”

Section: Related Workmentioning

confidence: 99%

Negative Variables and the Essence of Object-Oriented Programming

Meyer

Kogtenkov

2014

Specification, Algebra, and Software

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Reasoning about object-oriented programs requires an appropriate technique to reflect a fundamental "general relativity" property of the approach: every operation is relative to a current object, which changes with every qualified call; such a call needs access to the context of the client object. The notion of negative variable, discussed in this article, provides a framework for reasoning about OO programs in any semantic framework. We introduce a fundamental rule describing the semantics of object-oriented calls, its specific versions for such frameworks as axiomatic (Hoare-style) logic and denotational semantics, and its application to such problems as alias analysis and the consistency of concurrent programs. The approach has been implemented as part of a verification environment for a major object-oriented language and used to perform a number of proofs and analyses.

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Towards a Calculus of Object Programs

Cited by 5 publications

References 20 publications

Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)

Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)

LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification

Negative Variables and the Essence of Object-Oriented Programming

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