| Challenge: | Sentences with gapping lack an overt predicate to indicate the relation between two or more arguments. |
| Approach: | They propose two methods for parsing to a Universal Dependencies graph representation that explicitly encodes the elided material with additional nodes and edges. |
| Outcome: | The proposed methods reconstruct elided material from dependency trees with high accuracy when the parser correctly predicts the existence of a gap. |
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Parsing Gapping Constructions Based on Grammatical and Semantic Roles (2020.emnlp-main)
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| Challenge: | Existing methods for parsing sentences with gapping recover elided elements from redundant elements . grammatical and semantic tags are used to identify gaps in a coordinated structure . |
| Approach: | They propose a method of parsing sentences with gapping to recover elided elements . they use constituent trees annotated with grammatical and semantic roles . |
| Outcome: | The proposed method outperforms the previous method in terms of F-measure and recall. |
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13) (D19-53)
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| Challenge: | TextGraphs is a workshop on graph-based methods for natural language processing . the workshop is being organized in conjunction with the 9th International Joint Conference on Natural Language Processing . |
| Approach: | TextGraphs is the 13th edition of the Workshop on Graph-Based Methods for Natural Language Processing . the workshop promotes synergy between GT and natural language processing . |
| Outcome: | the 2013 edition of TextGraphs is being held in conjunction with the 9th International Joint Conference on Natural Language Processing in Hong Kong. |
Some Languages Seem Easier to Parse Because Their Treebanks Leak (2020.emnlp-main)
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| Challenge: | Cross-language differences in (universal) dependency parsing performance are mostly attributed to treebank size, average sentence length, average dependency length, morphological complexity, and domain differences. |
| Approach: | They compute graph isomorphisms and find that treebank size is a factor that influences parsing performance. |
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Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations (D19-3)
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| Challenge: | Proceedings of the system demonstrations session were presented at the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) EMNMP-IjCNLP 2019 has a Best Demo Award for the first time . |
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Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations (P18-1)
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| Challenge: | Existing methods for paraphrasing nouncompounds lack the ability to generalize and have a hard time interpreting infrequent or new noun-compound. |
| Approach: | They propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrase. |
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On Parsing as Tagging (2022.emnlp-main)
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| Challenge: | Existing approaches to reduce constituency parsing to tagging are based on linearization, learning, and decoding . linearization of the derivation tree is the most critical factor in achieving accurate parsers as taggers . |
| Approach: | They propose a pipeline with three steps for reducing constituency parsing to tagging . they find that linearization and learning are critical factors for accurate parsers . |
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An Empirical Study of Building a Strong Baseline for Constituency Parsing (P18-2)
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| Challenge: | Sequence-to-sequence models have been used for natural language generation tasks such as machine translation and summarization. |
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Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts (2021.acl-tutorials)
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| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
High-order Joint Constituency and Dependency Parsing (2024.lrec-main)
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| Challenge: | Syntactic parsing aims to reveal how sentences are syntactically structured. |
| Approach: | They propose to produce compatible constituency and dependency trees simultaneously for input sentences . they adopt a much more efficient decoding algorithm and explore joint modeling at training phase . |
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Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs (2023.emnlp-main)
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| Challenge: | Recent studies on relation representation learning focus on contrastive learning strategies, but these studies overlook important aspects. |
| Approach: | They propose to use within-sentence pairs augmentation and cross-sentent pairs extraction to increase diversity of positive pairs and strengthen the discriminative power of contrastive learning. |
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