| Challenge: | Pretrained Transformer encoders are the dominant approach to sequence labeling . however, few have been applied to sequence labels on flat or simplified tasks . |
| Approach: | They propose to use pretrained Transformer encoders to model relations across words . they find that the architectures adapt well across tagging tasks that vary in complexity . |
| Outcome: | The proposed architectures perform well across tagging tasks across languages and datasets. |
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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 . |
| Outcome: | The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders. |
Design Challenges and Misconceptions in Neural Sequence Labeling (C18-1)
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| Challenge: | Existing neural sequence labeling models have been used for many tasks such as POS tagging, chunking and named entity recognition (NER). |
| Approach: | They propose to replicate twelve neural sequence labeling models and compare them to three benchmarks to find out which models are effective and which are inconsistent. |
| Outcome: | The proposed models are compared on NER, Chunking, and POS tagging benchmarks. |
Viable Dependency Parsing as Sequence Labeling (N19-1)
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| Challenge: | Existing work on dependency parsing by sequence labeling suggested that it was impractical. |
| Approach: | They propose to use dependency trees as sequence labels to obtain fast and accurate parsers using a conventional BILSTM-based model. |
| Outcome: | The proposed models are conceptually simple, not needing traditional parsing algorithms or auxiliary structures, and provide a good speed-accuracy tradeoff, with results competitive with more complex approaches. |
Small and Practical BERT Models for Sequence Labeling (D19-1)
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| Challenge: | Existing models for morphosyntactic tagging have focused on building separate models for each language or for a small group of related languages. |
| Approach: | They propose a scheme to train a single multilingual sequence labeling model that is small and fast enough to run on a CPU. |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource languages and low-level models on codemixed inputs. |
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. |
Diverse Pretrained Context Encodings Improve Document Translation (2021.acl-long)
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| Challenge: | Existing models for sentence-level sequence-to-sequence translations do not use extra-sentential information. |
| Approach: | They propose a sentence-level sequence-to-sequence transformer with multiple pre-trained context signals. |
| Outcome: | The proposed model outperforms existing models on Chinese-English and English-German tasks. |
Which *BERT? A Survey Organizing Contextualized Encoders (2020.emnlp-main)
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| Challenge: | a survey on language representation learning aims to highlight common themes . we focus on the areas of progress, compared to other fields, and discuss how each area is evaluated. |
| Approach: | They present a survey on language representation learning to highlight common themes . they compare contributions in contextualized text encoders to ideas from other fields . |
| Outcome: | The proposed survey aims to highlight common themes in the field of language representation learning. |
A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling (P18-1)
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| Challenge: | Existing studies have shown that multi-task learning can boost the performance of related tasks such as MT and abstractive text summarization. |
| Approach: | They propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling. |
| Outcome: | The proposed architecture achieves 4.3%-50.5% absolute gains compared to mono-lingual model . the proposed model is particularly effective in low-resource settings . |
Hierarchical Bracketing Encodings Work for Dependency Graphs (2025.emnlp-main)
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| Challenge: | Sequence labeling (SL) is a simple yet effective paradigm for a wide range of natural language problems. |
| Approach: | They propose a new bracketing approach for dependency graph parsing that encodes graphs as sequences and n tagging actions. |
| Outcome: | The proposed approach significantly reduces label space while preserving structural information. |
Distillation of encoder-decoder transformers for sequence labelling (2023.findings-eacl)
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| Challenge: | despite the strong trend in NLP to explore the use of large language models, there is still limited work evaluating prompting and decoding mechanisms for SL tasks. |
| Approach: | They propose a hallucination-free framework for sequence tagging that is especially suited for distillation. |
| Outcome: | The proposed framework performs well across multiple sequence labelling datasets and in a few-shot learning scenario. |