Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing (2023.tacl-1)
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| Challenge: | Sequence-to-Sequence (S2S) models have been successful on text generation tasks . however, learning complex structures with S2S models remains challenging . |
| Approach: | They propose to use constrained decoding to model part-of-speech tagging, named entity recognition, constituency, and dependency parsing tasks with 3 lexically diverse linearization schemas and corresponding constrained coding methods. |
| Outcome: | The proposed methods outperform the state-of-the-art on four core tasks. |
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| Challenge: | Existing efforts to speed up constituent parsing have focused on chart-based or shift-reduce parsers. |
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Transforming Sequence Tagging Into A Seq2Seq Task (2022.emnlp-main)
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| Challenge: | Pretrained, large, generative language models have had great success in a wide range of sequence tagging and structured prediction tasks. |
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Bringing Emerging Architectures to Sequence Labeling in NLP (2026.eacl-long)
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| Challenge: | Pretrained Transformer encoders are the dominant approach to sequence labeling . however, few have been applied to sequence labels on flat or simplified tasks . |
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Compositional Generalization via Semantic Tagging (2021.findings-emnlp)
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| Challenge: | Existing neural sequence-to-sequence models fail at compositional generalization, i.e., they cannot generalize to unseen compositions of seen components. |
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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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Parsing linearizations appreciate PoS tags - but some are fussy about errors (2022.aacl-short)
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| Challenge: | Recent work on the impact of PoS tags on graph- and transition-based parsers suggests that they are only useful when tagging accuracy is prohibitively high or in low-resource scenarios. |
| Approach: | They examine the impact of PoS tags on graph- and transition-based parsers and propose to use them in a new paradigm for sequence labeling. |
| Outcome: | The proposed model is best when tagging accuracy and resource availability are high. |
Dependency Parsing via Sequence Generation (2022.findings-emnlp)
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| Challenge: | Existing methods for dependency parsing are transition-based, graph-based and sequence-to-sequence method. |
| Approach: | They propose to achieve dependency parsing (DP) via Sequence Generation (SG) by utilizing only the pre-trained language model without any auxiliary structures. |
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More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)
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| Challenge: | Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective . |
| Approach: | They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages . |
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Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT (2020.emnlp-main)
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| Challenge: | a new method of analysis based on semantic tags demonstrates that character-level representations improve performance across a subset of selected semantic phenomena. |
| Approach: | They combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. |
| Outcome: | The proposed model improves performance on a subset of selected semantic phenomena. |
What can we learn from Semantic Tagging? (D18-1)
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| Challenge: | a recent study shows that multi-task learning improves performance of NLP tasks by exploiting similarities between tasks. |
| Approach: | They employ semantic tagging as an auxiliary task for three NLP tasks . they compare full neural network sharing, partial neural network shared and learning what to share . |
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