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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Integrating Visuospatial, Linguistic, and Commonsense Structure into Story Visualization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bansal, Mohit; Maharana, Adyasha. - : Underline Science Inc., 2021 BASE Show details 2 Analysis of Tree-Structured Architectures for Code Generation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Bansal, Mohit; Dahal, Samip; Maharana, Adyasha. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.384 Abstract: Code generation is the task of generating code snippets from input user specifications in natural language. Leveraging the linguistically-motivated hierarchical structure of the input can benefit code generation, especially since the specifications are complex sentences containing multiple variables and operations over various data structures. Moreover, recent advances in Transformer architectures have led to improved performance with tree-to-tree style generation for other seq2seq tasks e.g., machine translation. Hence, we present an empirical analysis of the significance of input parse trees for code generation. We run text-to-tree, linearized tree-to-tree, and structured tree-to-tree models, using constituency-based parse trees as input, where the target is Abstract Syntax Tree (AST) of the code. We evaluate our models on the Python-based code generation dataset CoNaLa and a semantic parsing dataset ATIS. We find that constituency ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Neural Network; Semantics URL: https://underline.io/lecture/26475-analysis-of-tree-structured-architectures-for-code-generation https://dx.doi.org/10.48448/yhxr-pm37 BASE Hide details

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