F1-SCORES

Nested Named Entity Recognition via Second-best Sequence Learning and Decoding (2020.tacl-1)

Challenge:	Arguments contain subtexts, but they are connotative and need prompts to be recognized . a lightweight subtext generator is helpful when the prompt doesn't raise a complex CoT.
Approach:	They leverage LLaMA to generate subtexts for argument pairs and verify their effectiveness . they construct a baseline IDRR using the decoder-only backbone LLama .
Outcome:	The proposed approach achieves higher F1 scores on two benchmarks than previous models.

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Takashi Shibuya, Eduard Hovy

Fact from Fiction: Finding Serialized Novels in Newspapers (2025.acl-srw)

Challenge:	Named entity recognition (NER) is the task of identifying text spans associated with proper names and classifying them according to their semantic class such as person or organization.
Approach:	They propose a method that treats the tag sequence for nested entities as the second best path within the span of their parent entity.
Outcome:	The proposed method achieves F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE 2005, and GENIA datasets.

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Pascale Feldkamp, Alie Lassche, Katrine Frøkjær Baunvig, Kristoffer Nielbo, Yuri Bizzoni

Challenge:	Among underrepresented but widely read forms are serialized fiction and feuilleton novels embedded in newspapers rather than published as standalone volumes.
Approach:	They propose to annotate 1,394 articles and evaluate classification pipelines using both selected linguistic features and embeddings to identify serialized fiction and feuilleton fiction.
Outcome:	The proposed methods achieve F1-scores of 0.91 in an annotated dataset of 1,394 articles and support the construction of alternative literary corpora and contribute to work on modeling the fiction–nonfiction boundary at scale.

ViHOS: Hate Speech Spans Detection for Vietnamese (2023.eacl-main)

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Phu Gia Hoang, Canh Duc Luu, Khanh Quoc Tran, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen

Responsible & Trustworthy NLP

Challenge:	Increasing use of social networking sites can cause problems for human moderators to review tagged comments.
Approach:	They present a dataset that contains 26k spans on 11k comments and detailed annotation guidelines . they also provide definitions of hateful and offensive spans in Vietnamese comments .
Outcome:	The proposed dataset shows that it is difficult to detect specific types of spans in the dataset . the dataset is the first human-annotated corpus containing 26k spans on 11k comments .

Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging (2022.findings-acl)

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Yutai Hou, Cheng Chen, Xianzhen Luo, Bohan Li, Wanxiang Che

Retrieve and Copy: Scaling ASR Personalization to Large Catalogs (2023.emnlp-industry)

Challenge:	Recent results show that prompting methods are inefficient for slot tagging tasks . inverse prompting only requires a one-turn prediction for each slot type .
Approach:	They propose an inverse prompting paradigm that reversely predicts slot values given slot types . the method is faster and significantly improves the effect on 10-shot setting .
Outcome:	The proposed method improves over 6.1 F1-scores on 10-shot setting and achieves new state-of-the-art performance.

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Sai Muralidhar Jayanthi, Devang Kulshreshtha, Saket Dingliwal, Srikanth Ronanki, Sravan Bodapati

Information Retrieval

Challenge:	End-to-end ASR models struggle to recognize uncommon domain-specific words due to limited audio context.
Approach:	They propose a "Retrieve and Copy" mechanism to improve latency while retaining the accuracy even when scaled to a large catalog.
Outcome:	The proposed method achieves 6% more word error rate reduction and 3.6% improvement in F1 when scaled to a large catalog size while retaining the accuracy.

NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey (2026.findings-eacl)

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Dhiman Goswami, Jai Kruthunz Naveen Kumar, Sanchari Das

Responsible & Trustworthy NLP

Challenge:	Social media platforms such as X (formerly Twitter), Facebook, and Reddit generate user-generated content.
Approach:	They propose a framework to assess privacy risks in social media by evaluating vulnerabilities across six dimensions: data collection, preprocessing, visibility, fairness, computational risk, and regulatory compliance.
Outcome:	The proposed framework assesses privacy risks across six dimensions . it achieves F1-scores of 0.58–0.84, but incurs 1% - 23% drop under fine-tuning .

To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings (2021.acl-long)

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Sarah Moeller, Ling Liu, Mans Hulden

Syntactic Text Processing

Challenge:	Part-of-Speech (POS) tags are routinely included in many NLP tasks.
Approach:	They propose to use POS tags to examine morphological learning in low-resource languages . they find that POS tagging improves joint segmentation and glossing .
Outcome:	The proposed task is tested on two identical datasets with the Transformer architecture.

Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers (2022.findings-acl)

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Jakob Smedegaard Andersen, Walid Maalej

Information Retrieval

Challenge:	A series of benchmarking experiments based on three different datasets and three state-of-the-art classifiers show that our framework can improve the classification F1-scores by 5.1 to 11.2% (up to approx. 98 to 99%)
Approach:	They propose a semi-automated approach that passes unconfident, probably incorrect classifications to human moderators to minimize the workload.
Outcome:	The proposed approach can improve the classification F1-scores by 5.1 to 11.2% (up to approx. 98 to 99%) while reducing the moderation load up to 73.3% compared to a random moderation.

GLGR: Question-aware Global-to-Local Graph Reasoning for Multi-party Dialogue Reading Comprehension (2023.findings-emnlp)

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Yanling Li, Bowei Zou, Yifan Fan, Xibo Li, Ai Ti Aw, Yu Hong

Contextual String Embeddings for Sequence Labeling (C18-1)

Challenge:	Existing approaches for multi-hop reasoning are lacking for local graph reasoning . existing approaches neglect local semantic structures in utterances .
Approach:	They propose a question-aware global-to-local graph reasoning approach that expands the canonical Interlocutor-Utterance graph by introducing a query node.
Outcome:	The proposed approach outperforms existing methods on Molweni and FriendsQA.

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Alan Akbik, Duncan Blythe, Roland Vollgraf

Semantic Text Processing

Challenge:	Recent advances in language modeling have made it viable to model language as distributions over characters.
Approach:	They propose to leverage internal states of a trained character language model to produce a new type of word embeddings.
Outcome:	The proposed embeddings outperform the state-of-the-art on four classic sequence labeling tasks.

Smart “Chef”: Verifying the Effect of Role-based Paraphrasing for Aspect Term Extraction (2023.findings-emnlp)

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Jiaxiang Chen, Yu Hong, Qingting Xu, Jianmin Yao

Fine-Grained Error Analysis and Fair Evaluation of Labeled Spans (2022.lrec-1)

Challenge:	Aspect Term Extraction (ATE) is a task of automatically extracting aspect terms from sentences.
Approach:	They propose to automatically rewrite sentences from virtual experts with different roles . they leverage ChatGPT to determine virtual experts in the considered domains .
Outcome:	The proposed method can be used to expand the predictions obtained on the original sentences without retraining or fine-tuning the baseline extractors.

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Katrin Ortmann

Challenge:	Annotations with incorrect label or boundaries count as two errors instead of one, despite being closer to the target annotation than false positives or false negatives.
Approach:	They propose an algorithm for error identification in flat and multi-level annotations and propose a procedure for calculating meaningful precision, recall, and F1-scores based on the more fine-grained error types.
Outcome:	The proposed procedure prevents double penalties and allows for a more detailed error analysis, providing more insight into the actual weaknesses of a system.

Online Near-Duplicate Detection of News Articles (2020.lrec-1)

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Simon Rodier, Dave Carter

Responsible & Trustworthy NLP

Challenge:	Near-duplicate documents are prevalent in news corpora and cost significant . bloating corporata with redundant information and computational costs are among the costs .
Approach:	They propose an online system which flags a near-duplicate document by finding its most likely original.
Outcome:	The proposed system can be used in many real-world applications.

SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models (2024.acl-long)

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Md Imbesat Rizvi, Xiaodan Zhu, Iryna Gurevych

Identifying the Human Values behind Arguments (2022.acl-long)

Challenge:	Existing large language models (LLMs) do not perform well on the datasets.
Approach:	They propose to use a Spatial Reasoning Characterization framework and a spatial reasoning path framework to study spatial reasoning.
Outcome:	The proposed framework and datasets outperform state-of-the-art models in spatial reasoning.

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Johannes Kiesel, Milad Alshomary, Nicolas Handke, Xiaoni Cai, Henning Wachsmuth, Benno Stein

A Generalized Approach to Protest Event Detection in German Local News (2022.lrec-1)

Challenge:	et al., 2003) examines human values in natural language arguments . authors provide a dataset of 5270 arguments from four geographical cultures .
Approach:	They propose a multi-level taxonomy of human values with 54 values and a dataset of 5270 arguments from four geographical cultures, manually annotated for human values.
Outcome:	The proposed model shows that human values are more diverse than previously thought . it shows that people disagree on the best course forward on controversial issues .

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Gregor Wiedemann, Jan Matti Dollbaum, Sebastian Haunss, Priska Daphi, Larissa Daria Meier

CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset (2023.emnlp-main)

Challenge:	Social scientists conduct protest event analysis to learn about developments and trends of the forms, scale and hot topics of political protests.
Approach:	They propose to use a German language resource to analyze newspaper articles on protest events . they train and evaluate transformer-based text classifiers to automatically detect relevant newspaper articles .
Outcome:	The proposed method achieves a binary F1-score of 93.3 %, but does not generalize well to other datasets.

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Susanna Rücker, Alan Akbik

Aspect-based Document Similarity for Research Papers (2020.coling-main)

Challenge:	Existing models achieve F1-scores comparable to or exceed noise level in CoNLL-03 . current models have significant annotation errors, incompleteness, and inconsistencies in the data .
Approach:	They propose to add a layer of entity linking annotation to the CoNLL-03 corpus to correct 7.0% of all labels.
Outcome:	The proposed approach corrects 7.0% of all labels in the English CoNLL-03 dataset.

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Malte Ostendorff, Terry Ruas, Till Blume, Bela Gipp, Georg Rehm

Exploring the Potential of Large Language Models (LLMs) for Low-resource Languages: A Study on Named-Entity Recognition (NER) and Part-Of-Speech (POS) Tagging for Nepali Language (2024.lrec-main)

Challenge:	Traditional document similarity measures do not consider in what aspects two documents are similar.
Approach:	They extend document similarity with aspect information by performing a pairwise document classification task.
Outcome:	The proposed approach is best performing on 172,073 research paper pairs from the ACL Anthology and CORD-19 corpus.

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Bipesh Subedi, Sunil Regmi, Bal Krishna Bal, Praveen Acharya

Incorporating Zoning Information into Argument Mining from Biomedical Literature (2022.lrec-1)

Challenge:	Large Language Models excel in various tasks like Named Entity Recognition and Part-of-Speech tagging.
Approach:	They propose to use large language models to perform NLP tasks such as Named Entity Recognition and Part-of-Speech tagging in Nepali.
Outcome:	The proposed models perform better than other approaches for Nepali NER and POS tagging tasks.

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Boyang Liu, Viktor Schlegel, Riza Batista-Navarro, Sophia Ananiadou

German SRL: Corpus Construction and Model Training (2024.lrec-main)

Challenge:	Argumentative zoning is a text zonation scheme that is used to segment text into zones that serve distinct functions.
Approach:	They propose to use zoning information to incorporate into argument mining tasks . they add zonation labels predicted by an off-the-shelf model to the beginning of each sentence .
Outcome:	The proposed models improve argument mining models without additional annotation cost.

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Maxim Konca, Andy Luecking, Alexander Mehler

Multilinguality

Challenge:	Existing semantic role annotation resources are lacking for German.
Approach:	They propose a translation-based approach to train German semantic role models using semantic annotations and alignment models.
Outcome:	The proposed method achieves competitive evaluation scores, but avoids limitations of previous approaches.

Reproducing a Morphosyntactic Tagger with a Meta-BiLSTM Model over Context Sensitive Token Encodings (2020.lrec-1)

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Yung Han Khoe

Syntactic Text Processing

Challenge:	Reproducibility of research results is only recently beginning to be practiced and acknowledged . a research culture that focuses on beating previous benchmarks while disregarding the need to contribute to scientific knowledge and understanding is a problem, says a researcher.
Approach:	They reproduced work on morphosyntactic tagging using a meta-model . they did not contact the original authors for reproduction .
Outcome:	The proposed model outperforms previous models on morphological tagging tasks but fails to match the F1-scores reported for the meta-BiLSTM model.

Architectural Sweet Spots for Modeling Human Label Variation by the Example of Argument Quality: It’s Best to Relate Perspectives! (2023.emnlp-main)

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Philipp Heinisch, Matthias Orlikowski, Julia Romberg, Philipp Cimiano

Challenge:	Existing approaches to subjectivity in natural language processing are subjective . authors argue that disagreement should not be regarded as a problem .
Approach:	They propose to account for subjective perspectives of individuals and objective concepts that build a common ground between annotators.
Outcome:	The proposed architectures increase the averaged annotator-individual F1-scores up to 43% over a majority-label model.

LLMSegm: Surface-level Morphological Segmentation Using Large Language Model (2024.lrec-main)

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Marko Pranjić, Marko Robnik-Šikonja, Senja Pollak

Syntactic Text Processing

Challenge:	Existing approaches to morphological segmentation split word into its morphemes . LLMSegm is applicable in low-data settings and low-resourced languages .
Approach:	They propose a novel approach to surface-level morphological segmentation leveraging large language models.
Outcome:	The proposed method is applicable in low-data settings and low-resource languages.

Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents (2024.lrec-main)

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Ramona Christen, Anastassia Shaitarova, Matthias Stürmer, Joel Niklaus

Multilinguality

Challenge:	Negation scope resolution is a challenging task for NLP because of the complexity of legal texts and lack of annotated in-domain negation corpora.
Approach:	They propose to use annotated court decisions to improve negation scope resolution . they release annotations in german, french, and italian to train models without legal data .
Outcome:	The proposed models achieve token-level F1-scores of up to 86.7% in zero-shot and multilingual settings.

SGCM: Salience-Guided Context Modeling for Question Generation (2024.lrec-main)

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Chuyao Ding, Yu Hong, Jianmin Yao

LLM-FK: Multi-Agent LLM Reasoning for Foreign Key Detection in Large-Scale Complex Databases (2026.findings-acl)

Challenge:	Identifying relevant sentences to answers is crucial for reasoning the possible questions before generation.
Approach:	They propose a salience-guided approach to enhance Paragraph-level Question Generation by identifying salient sentences that manifest relevance.
Outcome:	The proposed approach achieves Rouge-L, BLEU4, BERTScore, Q-BLUE-3 and F1-scores compared to baseline on FairytaleQA.

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Zijian Tang, Ying Zhang, Sibo Cai, Ruoxuan Wang