Abstract syntax
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A Syntactic Neural Model for General-Purpose Code Generation Open
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 consider…
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Abstract Syntax Networks for Code Generation and Semantic Parsing Open
Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs a…
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Bill Hillier’s Legacy: Space Syntax—A Synopsis of Basic Concepts, Measures, and Empirical Application Open
Bill Hillier’s space syntax method and theory enables us to describe the spatial properties of a sustainable city. Empirical testing of the space syntax method over time has confirmed the capacity and innovativeness of analyzing spatial re…
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Deep learning similarities from different representations of source code Open
Assessing the similarity between code components plays a pivotal role in a number of Software Engineering (SE) tasks, such as clone detection, impact analysis, refactoring, etc. Code similarity is generally measured by relying on manually …
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Software Defect Prediction via Attention-Based Recurrent Neural Network Open
In order to improve software reliability, software defect prediction is applied to the process of software maintenance to identify potential bugs. Traditional methods of software defect prediction mainly focus on designing static code metr…
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Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge Open
In this paper, we propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge. We first build a syntax fusion encoder for encoding syntactic features, including a label-aware …
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A Syntactic Neural Model for General-Purpose Code Generation Open
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 consider…
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Compositionality in animals and humans Open
A key step in understanding the evolution of human language involves unravelling the origins of language's syntactic structure. One approach seeks to reduce the core of syntax in humans to a single principle of recursive combination, merge…
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A deep tree-based model for software defect prediction Open
Defects are common in software systems and can potentially cause various problems to software users. Different methods have been developed to quickly predict the most likely locations of defects in large code bases. Most of them focus on d…
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A Unified Syntax-aware Framework for Semantic Role Labeling Open
Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence. Syntactic information has been paid a great attention over the role of enhancing SRL. However, the latest advance shows that syntax would not be…
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Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks Open
We present a method for automatically generating repair feedback for syntax errors for introductory programming problems. Syntax errors constitute one of the largest classes of errors (34%) in our dataset of student submissions obtained fr…
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Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs Open
Code completion has become an essential component of integrated development environments. Contemporary code completion methods rely on the abstract syntax tree (AST) to generate syntactically correct code. However, they cannot fully captur…
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SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation Open
Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code intelligence. Recently, many pre-trained language models for so…
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Source Code Assessment and Classification Based on Estimated Error Probability Using Attentive LSTM Language Model and Its Application in Programming Education Open
The rate of software development has increased dramatically. Conventional compilers cannot assess and detect all source code errors. Software may thus contain errors, negatively affecting end-users. It is also difficult to assess and detec…
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Abella: A System for Reasoning about Relational Specifications Open
The Abella interactive theorem prover is based on an intuitionistic logic that allows for inductive and co-inductive reasoning over relations. Abella supports the λ-tree approach to treating syntax containing binders: it allows simply type…
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Correlating Neural and Symbolic Representations of Language Open
Analysis methods which enable us to better understand the representations and\nfunctioning of neural models of language are increasingly needed as deep\nlearning becomes the dominant approach in NLP. Here we present two methods\nbased on R…
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Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation Open
We present a parser for Abstract Meaning Representation (AMR).We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT).To make this work, we transform the AMR structure into a form s…
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Stack-propagation: Improved Representation Learning for Syntax Open
Traditional syntax models typically leverage part-of-speech (POS) information by constructing features from hand-tuned templates.We demonstrate that a better approach is to utilize POS tags as a regularizer of learned representations.We pr…
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Getting More Out Of Syntax with PropS Open
Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed expressed by syntax, many phenomena are not easily read out of dep…
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Syntax in the Treetops Open
A proposal that syntax extends to the domain of discourse in making core syntax link to the conversational context. In Syntax in the Treetops, Shigeru Miyagawa proposes that syntax extends into the domain of discourse by making linkages be…
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Deep Learning With Customized Abstract Syntax Tree for Bug Localization Open
Given a bug report, bug localization technique can help developers automatically locate potential buggy files. Information retrieval and deep learning approaches have been applied in bug localization by extracting lexical features in bug r…
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Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) Open
In recent years, the software industry has invested substantial effort to improve software quality in organizations. Applying proactive software defect prediction will help developers and white box testers to find the defects earlier, and …
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Exploring Software Naturalness through Neural Language Models Open
The Software Naturalness hypothesis argues that programming languages can be understood through the same techniques used in natural language processing. We explore this hypothesis through the use of a pre-trained transformer-based language…
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Relation Extraction with Convolutional Network over Learnable Syntax-Transport Graph Open
A large majority of approaches have been proposed to leverage the dependency tree in the relation classification task. Recent works have focused on pruning irrelevant information from the dependency tree. The state-of-the-art Attention Gui…
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Syntax Encoding with Application in Authorship Attribution Open
We propose a novel strategy to encode the syntax parse tree of sentence into a learnable distributed representation. The proposed syntax encoding scheme is provably information-lossless. In specific, an embedding vector is constructed for …
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Structural Language Models of Code Open
We address the problem of any-code completion - generating a missing piece of source code in a given program without any restriction on the vocabulary or structure. We introduce a new approach to any-code completion that leverages the stri…
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WASTK: A Weighted Abstract Syntax Tree Kernel Method for Source Code Plagiarism Detection Open
In this paper, we introduce a source code plagiarism detection method, named WASTK (Weighted Abstract Syntax Tree Kernel), for computer science education. Different from other plagiarism detection methods, WASTK takes some aspects other th…
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An Abstract Syntax Tree Encoding Method for Cross-Project Defect Prediction Open
In the last few years, with the development of deep learning theory, researchers have tried to introduce the method of artificial intelligence into the field of software defect prediction (SDP) to improve its prediction effect. To be fed i…
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PathMiner: A Library for Mining of Path-Based Representations of Code Open
One recent, significant advance in modeling source code for machine learning algorithms has been the introduction of path-based representation - an approach consisting in representing a snippet of code as a collection of paths from its syn…
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A Neural Network Based Intelligent Support Model for Program Code Completion Open
In recent years, millions of source codes are generated in different languages on a daily basis all over the world. A deep neural network-based intelligent support model for source code completion would be a great advantage in software eng…