Papers with sequence labeling task
NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection (D19-57)
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| Challenge: | Named entity recognition has been extensively studied on English news texts, but transfer to other domains and languages is still a challenging problem. |
| Approach: | They propose a system that provides a non-standard domain and language setting for pharmacological entity detection in Spanish texts and a sequencelabeling task that requires neither language nor domain expertise. |
| Outcome: | The proposed system achieves up to 88.6% F1 in the PharmaCoNER competition and is based on a sequence labeling task and training on annotated data. |
Opinion Mining with Deep Contextualized Embeddings (N19-3)
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| Challenge: | Existing methods for opinion expression detection are based on token-level sequence labeling . |
| Approach: | They propose to use BERT and conditional random field embedders to detect opinion expressions. |
| Outcome: | The proposed model outperforms ELMo embedders in opinion expression detection. |
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields (2020.findings-emnlp)
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| Challenge: | Existing approaches to recover dropped pronouns ignore the dependencies between pronounes in neighboring utterances. |
| Approach: | They propose a framework that combines Transformer network and General Conditional Random Fields to model the dependencies between pronouns in neighboring utterances. |
| Outcome: | The proposed framework outperforms state-of-the-art models on three Chinese conversation datasets showing that it captures the dependencies between pronouns in neighboring utterances. |
DRS Parsing as Sequence Labeling (2022.starsem-1)
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| Challenge: | a new semantic parser for English, German, Italian, and Dutch discourse representation structures is developed . we present a system that maps tokens to finite set of meaning fragments and is more transparent . a comprehensive error analysis highlights areas for future work on semantic parses . |
| Approach: | They propose a fully trainable semantic parser for English, German, Italian, and Dutch discourse representation structures that maps each token to one of a finite set of meaning fragments. |
| Outcome: | The proposed system is more transparent and useful for human-in-the-loop annotations. |
NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension (2022.naacl-industry)
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| Challenge: | Named Entity Recognition (NER) is a task of locating and classifying entities mentioned in unstructured text into predefined categories. |
| Approach: | They propose to use a BERT-based multi-question MRC task where multiple questions (one question per entity) are considered at the same time for a single text. |
| Outcome: | The proposed architecture leads to 2.5 times faster training and 2.3 times faster inference on three NER datasets. |
Context-aware Stand-alone Neural Spelling Correction (2020.findings-emnlp)
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| Challenge: | Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. |
| Approach: | They propose a stand-alone spelling correction problem that corrects the spelling of tokens without additional token insertion or deletion. |
| Outcome: | The proposed solution outperforms the state-of-the-art spelling correction model by 12.8% absolute F0.5 score. |
Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts (2020.findings-emnlp)
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| Challenge: | Existing methods for dividing biomedical abstracts into rhetorical segments assign a rhetorical label to each sentence while considering context in the abstract. |
| Approach: | They propose to use Neural Semi-Markov Conditional Random Fields to assign a rhetorical label to a span that consists of continuous sentences. |
| Outcome: | The proposed method achieved the best micro sentence-F1 score and the best macro span-F1. |
Sub-event detection from twitter streams as a sequence labeling problem (N19-1)
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| Challenge: | Existing methods for sub-event detection do not account for sequential nature of social media streams. |
| Approach: | They propose to use a neural sequence architecture that explicitly accounts for the chronological order of posts to improve sub-event detection. |
| Outcome: | The proposed method outperforms a graph-based state-of-the-art method for binary sub-event detection (2.7% micro-F1 improvement) it also outperformed a recurrent neural network model on the posts sequence level for labeled sub- events (2.4% bin-level improvement). |
Treasures Outside Contexts: Improving Event Detection via Global Statistics (2021.emnlp-main)
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| Challenge: | Existing neural-based ED models are confused by changeable contexts during testing . we propose a system that extracts statistical event features from word-event cooccurrence frequencies . |
| Approach: | They propose to integrate a set of statistical event features from word-event co-occurrence frequencies into the training set to cooperate with contextual features. |
| Outcome: | The proposed model outperforms ten strong baselines on ACE2005 and KBP2015 datasets. |
GPT-NER: Named Entity Recognition via Large Language Models (2025.findings-naacl)
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Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rongbin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, Guoyin Wang, Chen Guo
| Challenge: | Large-scale language models (LLMs) have shown impressive ability for in-context learning with limited training data. |
| Approach: | They propose a novel sequence labeling task that transforms a sequence labeled as a text-generation task into a self-verification task that LLMs can adapt to. |
| Outcome: | The proposed model performs better on NER than supervised models on a variety of tasks . the proposed model can be easily adapted by LLMs to generate a text sequence . |
Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph (2021.emnlp-main)
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| Challenge: | Existing studies on argumentation mining focus on monological argumentation and dialogical argumentation. |
| Approach: | They propose a mutual guidance framework that could guide arguments in one passage . they propose an inter-sentence relation graph to effectively model the inter-relations between two sentences . |
| Outcome: | The proposed method outperforms the current state-of-the-art model. |
On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles (2021.naacl-main)
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| Challenge: | citation worthiness is an emerging research topic in the natural language processing domain . citation recommendation systems are often approached as ranking problems . |
| Approach: | They propose a hierarchical biLSTM-based model that uses two adjacent sentences to solve a citation worthiness problem. |
| Outcome: | The proposed approach can be applied to a dataset of over two million sentences and their labels. |
Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis (P19-1)
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| Challenge: | Cognitive task analysis (CTA) is a type of analysis used to elicit and represent the knowledge and thought processes of domain experts. |
| Approach: | They propose a weakly-supervised framework for automated CTA transcript parsing . they partition the parser process into a sequence labeling task and a text span-pair relation extraction task with distant supervision from human-curated protocol files. |
| Outcome: | The proposed framework reduces human labor and scales the task to a small scale. |
Phrase Grounding by Soft-Label Chain Conditional Random Field (D19-1)
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| Challenge: | Existing methods to ground entities depend on inference or non-differentiable losses. |
| Approach: | They propose a phrase grounding task that grounds entities to corresponding regions in an image . they use neural chain Conditional Random Fields to model dependencies among regions . |
| Outcome: | The proposed method is based on a dataset of the Flickr30k Entities dataset. |
Nested Named Entity Recognition as Single-Pass Sequence Labeling (2025.findings-emnlp)
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| Challenge: | nested named entity recognition is a sequence labeling task that can be trained using any off-the-shelf sequence labelling library. |
| Approach: | They use prior work that linearizes constituency structures to create a nested named entity recognition task. |
| Outcome: | The proposed method reduces the complexity of the predicted nested entity recognition problem to a simple token classification task. |
APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning (2020.emnlp-main)
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| Challenge: | Argument mining is an important research field that attracts growing attention in recent years. |
| Approach: | They propose a new task to extract argument pairs from peer review and rebuttal . they use an open review platform to analyze the contents, structure and connections . |
| Outcome: | The proposed task is based on a dataset of 4,764 fully annotated review-rebuttal passage pairs . it is able to detect argumentative propositions and extract argument pairs from the corpus . |
One2Set + Large Language Model: Best Partners for Keyphrase Generation (2024.emnlp-main)
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| Challenge: | Existing selection methods make redundant selections, causing poor recall and accuracy. |
| Approach: | They propose a framework to generate keyphrases from a one2set-based model and an LLM as selector. |
| Outcome: | The proposed framework surpasses state-of-the-art models in absent keyphrase prediction. |
T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks (2023.findings-emnlp)
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| Challenge: | Annotation projection is a task of transporting labels from source to target language without high-quality training data. |
| Approach: | They propose an annotation projection approach that leverages pretrained text2text models and machine translation technology to generate annotated data. |
| Outcome: | The proposed approach outperforms existing methods on intrinsic and extrinsic tasks in 5 Indo-European and 8 low-resource African languages. |