| Challenge: | Discourse representation tree structure (DRTS) parsing is a new semantic parser which ignores structural information. |
| Approach: | They propose a structural-aware model to integrate structural information into the model . they use graph attention network (GAT) to exploit structural information for effective modeling . |
| Outcome: | The proposed model can achieve the best performance on a benchmark dataset. |
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Discourse Representation Structure Parsing (P18-1)
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| Challenge: | Existing semantic parsers are data-driven using annotated examples consisting of utterances and their meaning representations. |
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Discourse Representation Parsing for Sentences and Documents (P19-1)
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| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
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Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)
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| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
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Learning Sequential and Structural Information for Source Code Summarization (2021.findings-acl)
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| Challenge: | Existing models that learn both sequential and structural features of source code are limited in their use. |
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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 . |
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Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
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| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
| Approach: | They propose a discourse parser that incorporates recent contextual language models to improve the performance of RST-based discourse parses. |
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Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020.findings-emnlp)
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| Challenge: | Graph2Tree model encodes graph-structured input and decodes tree-structures output. |
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Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
| Approach: | They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form . |
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A Tree-based Decoder for Neural Machine Translation (D18-1)
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| Challenge: | Existing work on adding syntactic information to NMT systems is limited to linguistically-inspired tree structures. |
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