| Challenge: | In state-of-the-art semantic parsers map natural language instructions to source code . idioms improve the accuracy of semantic parses, allowing for faster decoding . |
| Approach: | They propose an iterative method to extract code idioms from large source code corpora . they use most-frequent subtrees of their syntax trees to train semantic parsers to apply them . |
| Outcome: | The proposed method improves the state-of-the-art semantic parsers' accuracy and training time by more than 50%. |
Similar Papers
MAGPIE: A Large Corpus of Potentially Idiomatic Expressions (2020.lrec-1)
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| Challenge: | Existing corpora cover less than 5,000 instances of less than 100 different idiom types . large corpus allows for better evaluation of assumptions about idiomatic expressions . |
| Approach: | They propose to build the largest-to-date corpus of idioms for English using crowdsourcing methods. |
| Outcome: | The proposed corpus is larger than existing resources and contains rich metadata and is made publicly available. |
CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees (2021.emnlp-main)
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| Challenge: | Existing methods for code summarization do not capture rich information in ASTs . existing methods are labor-intensive and time-consuming to document code with good summaries manually. |
| Approach: | They propose a model that hierarchically splits and reconstructs ASTs by a neural network . they propose to use AST embeddings and a vanilla code token encoder to generate the model . |
| Outcome: | The proposed model splits and reconstructs ASTs into subtrees and then aggregates embeddings of subtreas to get the complete AST. |
Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressions (2022.tacl-1)
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| Challenge: | Prior work has identified deficiencies in their contextualized representation stemming from the underlying compositional paradigm of representation. |
| Approach: | They propose to use an adapter as a lightweight non-compositional language expert trained on idiomatic sentences to build idiomity into BART. |
| Outcome: | The proposed approach improves idiomaticity over baselines and up to 25% higher sequence accuracy on idiom processing tasks. |
Idiomatic Expression Identification using Semantic Compatibility (2021.tacl-1)
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| Challenge: | Existing approaches to localize idiomatic expressions have limited views of their generalizability to new idioms. |
| Approach: | They propose a multi-stage neural architecture to detect whether a sentence has an idiomatic expression and localize it when it occurs in a figurative sense. |
| Outcome: | The proposed model achieves state-of-the-art on three of the largest datasets with idiomatic expressions of varied syntactic patterns and degrees of non-compositionality. |
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy (2021.emnlp-main)
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Colin Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy
| Challenge: | Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks. |
| Approach: | They propose an architecture-independent approach for leveraging syntactic hierarchies of source code . they use syntax trees to extract syntak hierarchical structures and integrate them into context window . |
| Outcome: | The proposed approach achieves state-of-the-art in code completion and summarization for Python in the CodeXGLUE benchmark. |
Examining the Tip of the Iceberg: A Data Set for Idiom Translation (L18-1)
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| Challenge: | Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs. |
| Approach: | They propose to use a large-scale data set to evaluate idiom translation in GermanEnglish. |
| Outcome: | The proposed dataset is used to perform preliminary NMT experiments on idiom translation in GermanEnglish. |
Beyond Multiword Expressions: Processing Idioms and Metaphors (P18-5)
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| Challenge: | idioms and metaphors processing is a rapidly growing area in NLP, says dr. s. robertson . idiomatic idiomas are characteristic to all areas of human activity and to all types of discourse. |
| Approach: | This tutorial will provide attendees with a clear notion of idioms and metaphors . it will provide them with computational models of linguistic characteristics and methods . |
| Outcome: | This tutorial aims to provide attendees with a clear notion of the linguistic characteristics of idioms and metaphors . it outlines how to model idiomatic idiomes and their processing and what resources are available to support their use . |
Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting (2023.emnlp-main)
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| Challenge: | idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts. |
| Approach: | They propose to use retrieval-augmented models to increase the accuracy of a strong pretrained machine translation model on idiomatic sentences by up to 13%. |
| Outcome: | The proposed techniques improve the accuracy of a strong pretrained model on idiomatic sentences by up to 13% in absolute accuracy, and holds potential benefits for non-idiomatic phrases. |
Refining Idioms Semantics Comprehension via Contrastive Learning and Cross-Attention (2024.lrec-main)
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| Challenge: | Existing methods based on deep learning struggle to grasp idiom semantics due to the figurative meanings of many idiomas deviating from their literal interpretations. |
| Approach: | They propose a Chinese idiom cloze test to capture comprehensive idiomatics and a semantic sense contrastive learning module to enhance the representation of idiomics. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Chinese idiom cloze test and on other benchmark datasets. |
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing datasets are limited to providing the degree of idiomaticity of expressions along with the literal and, where applicable, (a single) non-literal interpretation of MWEs. |
| Approach: | They propose to use a dataset to test the effectiveness of a language model in generating representations of sentences containing idioms. |
| Outcome: | The proposed model performs reasonably well on the one-shot and few-shot scenarios, but there is scope for improvement in the zero-shot scenario. |