Papers with Tree-LSTM
Code Summarization with Structure-induced Transformer (2021.findings-acl)
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| Challenge: | Code summarization (CS) is a promising area in recent language understanding . previous work using structurebased traversal or non-sequential models to learn structural program semantics has shown no performance gain . |
| Approach: | They propose to use a structure-based traversal model to learn structural program semantics to generate human language automatically for programming language in the format of source code. |
| Outcome: | Experiments show that the proposed method achieves state-of-the-art on benchmarks. |
Biomedical Event Extraction based on Knowledge-driven Tree-LSTM (N19-1)
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| Challenge: | Biomedical event extraction requires domain-specific knowledge and deep understanding of complex contexts. |
| Approach: | They propose a knowledge base-driven tree-structured long short-term memory networks framework . tree-LSTM framework incorporates dependency structures and entity properties from ontologies . |
| Outcome: | The proposed framework is based on the BioNLP shared task with Genia dataset and achieves state-of-the-art results. |
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering (C18-1)
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| Challenge: | Sentence pair modeling is a fundamental technique underlying many NLP tasks. |
| Approach: | They analyze several neural network designs for sentence pair modeling and compare their performance extensively across eight datasets. |
| Outcome: | The proposed models perform well across eight datasets including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. |
Learning from Non-Binary Constituency Trees via Tensor Decomposition (2020.coling-main)
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| Challenge: | a binarisation procedure changes the structure of constituency trees, furthering constituents that are not binary. |
| Approach: | They propose a binarised approach to binarise constituency trees by tensor-based models . they propose 'trunk-LSTM' model which exploits such a rich structure . |
| Outcome: | The proposed model performs well on different NLP tasks. |