| Challenge: | Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. |
| Approach: | They propose to use Universal Dependencies and UCCA as test cases to compare syntactic and semantic schemes. |
| Outcome: | The proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. |
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| Challenge: | Several attempts have been made to jointly parse syntax and semantics, but this trade-off is not well understood. |
| Approach: | They propose multiple model architectures that exploit the rich syntactic and semantic annotations contained in the Universal Decompositional Semantics dataset to obtain state-of-the-art results. |
| Outcome: | The proposed model outperforms existing models in 8 languages and their results are consistent across languages. |
Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics (2020.coling-main)
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| Challenge: | a systematic comparative analysis of linguistic meaning representations from different frameworks is needed. |
| Approach: | They compare a rule-based converter and a supervised delexicalized parser to map meaning representations from different frameworks. |
| Outcome: | The proposed method yields surprisingly accurate representations close to fully supervised UCCA parser quality. |
Cross-lingual Semantic Representation for NLP with UCCA (2020.coling-tutorials)
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| Challenge: | introductory tutorial to UCCA, a symbolic meaning representation for semantic representations. |
| Approach: | This tutorial introduces UCCA, a cross-linguistically applicable framework for semantic representation . it will provide a detailed introduction to the UCca annotation guidelines, design philosophy and available resources . |
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Parsing All: Syntax and Semantics, Dependencies and Spans (2020.findings-emnlp)
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| Challenge: | Syntactic and semantic structures are key linguistic contextual clues, but few studies have explored how they can be used to improve syntactical parsing. |
| Approach: | They propose a syntactic and semantic parsing model which integrates syntaktic information in the encoder of neural network and benefits from two representation formalisms in a uniform way. |
| Outcome: | The proposed model achieves state-of-the-art or competitive results on both span and dependency representations and on Penn Treebank. |
On the Relation between Syntactic Divergence and Zero-Shot Performance (2021.emnlp-main)
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| Challenge: | Recent advances in cross-lingual transfer methods have enabled significant advances in grammatical processing tasks. |
| Approach: | They examine the extent to which syntactic relations are preserved in translation and parsability in a zero-shot setting. |
| Outcome: | The proposed model is based on a translation task in English and a subset of a standard English RE benchmark translated to Russian and Korean. |
Multitask Parsing Across Semantic Representations (P18-1)
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| Challenge: | UCCA parsing is a test case for multitask learning, with auxiliary tasks AMR, SDP and Universal Dependencies (UD) . Semantic parsers have arguably yet to reach their full potential due to the limited amount of semantically annotated training data. |
| Approach: | They propose a general transition-based parser that can parse UCCA, AMR, SDP and Universal Dependencies (UD) they use a transition-driven learning architecture and a uniform transition-basic learning architecture to train the parsers. |
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Comparing learnability of two dependency schemes: ‘semantic’ (UD) and ‘syntactic’ (SUD) (2021.findings-emnlp)
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| Challenge: | Several studies have suggested that choosing syntactic criteria for assigning heads in dependency trees improves the performance of dependency parsers. |
| Approach: | They propose to use syntactic criteria to assign heads to dependency trees to improve the performance of dependency parsers by using a selection of 21 treebanks. |
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On the Continued Value of Universal Dependencies in the Era of Large Language Models (2026.acl-long)
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| Challenge: | a growing belief that explicit linguistic representations are no longer necessary is questioned in large language models . a recent study examines whether and in what ways this cross-lingual syntactic framework can still benefit LLMs . |
| Approach: | They use Universal Dependencies (UD) to examine whether and in what ways it can still benefit LLMs. |
| Outcome: | The proposed model outperforms its syntax-agnostic counterparts in a cross-lingual evaluation task. |
Do UD Trees Match Mention Spans in Coreference Annotations? (2021.findings-emnlp)
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| Challenge: | Existing methods to annotate mention spans are based on delimiting token intervals, but there is no syntactic representation of the mention span. |
| Approach: | They propose to integrate coreference annotation with syntactic annotation to make them convergent in the long term. |
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Towards a Unified Taxonomy of Deep Syntactic Relations (2024.lrec-main)
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| Challenge: | Currently, UD is the standard for morphology and surface syntax annotations, but it is only one step towards natural language understanding. |
| Approach: | They propose to use a set of universal semantic role labels for morphology and surface syntax in four Indo-European and one Uralic languages to analyze the data. |
| Outcome: | The proposed set of universal semantic role labels is based on the data from four Indo-European and one Uralic languages. |