Papers with sequence labeling models
Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction (2022.emnlp-industry)
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| Challenge: | Existing reading comprehension models can over-generate attribute values which hinders precision. |
| Approach: | They propose a product attribute value extraction task that captures key factual information from product descriptions and a new end-to-end pipeline framework called Ask-and-Verify. |
| Outcome: | The proposed framework outperforms existing models by up to 3.1% F1 absolute improvement points while scaling to thousands of attributes. |
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition (2020.acl-srw)
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| Challenge: | In general, the labels used in sequence labeling consist of different types of elements. |
| Approach: | They propose to integrate label component information as embeddings into sequence labeling models. |
| Outcome: | The proposed method improves on English and Japanese fine-grained named entity recognition on low-frequency labels. |
A Boundary-aware Neural Model for Nested Named Entity Recognition (D19-1)
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| Challenge: | Existing methods for named entity recognition ignore nested entities . a boundary-aware neural model can locate entities precisely by detecting boundaries . |
| Approach: | They propose a boundary-aware neural model for nested named entity recognition which leverages entity boundaries to predict entity categorical labels. |
| Outcome: | The proposed model outperforms state-of-the-art methods on GENIA dataset . it captures dependencies of entity boundaries and categorical labels, which helps to improve identifying entities. |
Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks (2022.findings-naacl)
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| Challenge: | Code-switching dependency parsing is a challenging task due to the scarcity of necessary resources and structural difficulties embedded in code-switch languages. |
| Approach: | They propose to use sequence labeling models as auxiliary tasks for code-switched dependency parsing in a semi-supervised scheme and acquire state-of-the-art scores on all studied languages. |
| Outcome: | The proposed model outperforms the previous model by 7.4 LAS points on average on all of the studied languages. |
NAT: Noise-Aware Training for Robust Neural Sequence Labeling (2020.acl-main)
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| Challenge: | Sequence labeling systems should perform reliably under ideal conditions and with corrupted inputs. |
| Approach: | They propose two noise-aware training objectives that improve robustness of sequence labeling performed on perturbed inputs. |
| Outcome: | The proposed methods improve robustness on English and German named entity recognition benchmarks. |
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction (2022.coling-1)
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| Challenge: | Existing solutions for quotation extraction use rule-based approaches and sequence labeling models. |
| Approach: | They propose a Context and Former-Label Enhanced Net for quotation extraction. |
| Outcome: | The proposed method achieves state-of-the-art performance on complicated quotation extraction on two public datasets and one proprietary dataset. |
From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer (2022.acl-long)
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| Challenge: | a dataset of English posts with annotations of toxic spans is released . sequence labeling models perform best, but rationale extraction methods are promising . |
| Approach: | They propose a dataset for toxic spans detection that includes an annotation of toxic posts . they propose to add generic rationale extraction mechanisms to the model to obtain toxic span information . |
| Outcome: | The proposed framework is based on a dataset of English posts with toxic span annotations . it shows that sequence labeling models perform best, but that rationale extraction methods are promising . |
Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling (2022.acl-long)
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| Challenge: | a corpus of Spanish newswire rich in unassimilated lexical borrowings is used to identify the language of a word. |
| Approach: | They propose to annotate a corpus of Spanish newswire rich in unassimilated lexical borrowings and evaluate how models perform on this task. |
| Outcome: | The proposed model outperforms models fed with subword embeddings and Transformer-based embeddables on the Spanish newswire corpus. |
Grammatical Error Correction as GAN-like Sequence Labeling (2021.findings-acl)
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| Challenge: | Traditional GEC models learn from sentences with fixed error rates . sequence labeling approaches suffer from a couple of key problems . |
| Approach: | They propose a GAN-like sequence labeling model with a grammatical error detector and a generator to correct grammamatical errors. |
| Outcome: | The proposed model improves the state-of-the-art in GEC and improves on benchmarks. |
A Unified MRC Framework for Named Entity Recognition (2020.acl-main)
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| Challenge: | Named entity recognition is divided into nested NER and flat NER depending on whether entities are nesting. |
| Approach: | They propose to formulate named entity recognition task as machine reading comprehension task instead of sequence labeling problem . |
| Outcome: | The proposed framework achieves vast amount of performance boost over current models on nested and flat NER datasets. |
DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles (2022.lrec-1)
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| Challenge: | Existing methods to extract and attribute quotations from news data are difficult and require a lot of effort. |
| Approach: | They propose a corpus of 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media. |
| Outcome: | The proposed corpus contains 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media. |
SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling (2020.acl-main)
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| Challenge: | Empirical studies show that virtual adversarial training (VAT) significantly improves the sequence labeling performance over baselines under supervised and semi-supervised settings. |
| Approach: | They propose a method which naturally applies VAT to sequence labeling models with conditional random field (CRF) Empirical studies show that SeqVAT significantly improves the sequence labelling performance over baselines under supervised settings, and outperforms state-of-the-art approaches under semi-supervised settings. |
| Outcome: | Empirical results show that the proposed method outperforms state-of-the-art approaches under semi-supervised settings. |
Improved grammatical error correction by ranking elementary edits (2022.emnlp-main)
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| Challenge: | a new study shows that grammatical error correction models are far from perfect for English . reranking allows for a better classification of edits, but it can be difficult for other languages . |
| Approach: | They propose a two-stage reranking method for grammatical error correction using a model as edit generator and a sequence labeling model as second step. |
| Outcome: | The proposed method surpasses existing methods on BEA 2019 English dataset by 2-3%. |