Papers with binary classification task
Assessing In-context Learning and Fine-tuning for Topic Classification of German Web Data (2024.acl-srw)
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| Challenge: | Using a few hundred annotated data points per topic, we detect content related to three German policies in a database of scraped webpages. |
| Approach: | They propose to use annotated data to train a binary classification task to detect topic-related content in a scraped database of webpages. |
| Outcome: | The proposed model detects content related to three German policies in a scraped database of scrapes of webpages using a few hundred annotated data points per topic. |
Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs (C18-1)
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| Challenge: | Existing methods to assess text complexity only at lexical and syntactic levels have not been attempted. |
| Approach: | They propose to automatically estimate conceptual complexity using graph-based measures on a large knowledge base. |
| Outcome: | The proposed measures achieve high discriminative power even in a default setup. |
Understanding tables with intermediate pre-training (2020.findings-emnlp)
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| Challenge: | Textual entailment is well studied, but is less well studied for table enlargement . a new dataset of millions of examples is used to train the model . |
| Approach: | They adapt a table-based BERT model to recognize entailment from a dataset . they evaluate table pruning techniques as a pre-processing step to improve model efficiency . |
| Outcome: | The proposed model improves training and prediction efficiency at a moderate drop in accuracy. |
BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering (2023.acl-short)
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| Challenge: | Existing methods for commonsense reasoning use knowledge graphs to train models . however, it is not always possible to have relevant training data available . |
| Approach: | They propose to transform a question-answer task into a binary classification task by ranking all candidate answers according to their reasonableness. |
| Outcome: | The proposed approach is less data hungry than existing methods using KGs. |
Adapting Deep Learning Methods for Mental Health Prediction on Social Media (D19-55)
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| Challenge: | a quarter of the population in Europe suffers from an episode of a mental disorder in their life, according to the World Health Organization . text analysis of rich resources like social media can contribute to deeper understanding of mental health and provide means for their early detection. |
| Approach: | They propose to use a hierarchical attention network to predict if a user suffers from one of nine disorders to adapt a deep neural model to the task. |
| Outcome: | The proposed model outperforms previous benchmarks for four out of nine disorders in a binary classification task on social media. |
Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding (2022.findings-acl)
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| Challenge: | Existing work on decoding language from non-invasive brain activity is limited due to noisy nature of brain recordings. |
| Approach: | They propose a cross-modal cloze task to predict a word from a neural image . they use a pre-trained language model to leverage the pre-training language model . |
| Outcome: | The proposed method outperforms baselines on 20 participants from two brain imaging datasets. |
Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks (P19-2)
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| Challenge: | Detection of adverse drug reactions in post-marketing period is a crucial challenge for pharmacology. |
| Approach: | They propose to use social media to extract information about adverse drug reactions . they compare four state-of-the-art attention-based neural networks to the F-measure . |
| Outcome: | The proposed methods perform better on four different benchmarks. |
Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification (2026.acl-srw)
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| Challenge: | idiomatic light verb constructions (LVCs) are challenging for multiword expression processing . they share the same surface form as fully literal verb–object combinations . |
| Approach: | They frame Turkish LVC detection as a binary classification task . they compare a supervised Turkish encoder baseline to three instruction-tuned LLMs . |
| Outcome: | The proposed method improves Turkish LVC detection on a controlled set with matched negatives and positives. |
Bicleaner AI: Bicleaner Goes Neural (2022.lrec-1)
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| Challenge: | a new version of Bicleaner detects noisy sentences in parallel corpora . the tool is based on pre-trained transformer-based language models fine-tuned on a binary classification task. |
| Approach: | They propose to use Bicleaner AI to detect noisy sentences in parallel corpora . they use pre-trained transformer-based language models fine-tuned on a binary classification task . |
| Outcome: | The proposed tool improves translation quality and reduces manual cleaning steps. |
An Expert Annotated Dataset for the Detection of Online Misogyny (2021.eacl-main)
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| Challenge: | Existing studies have found that misogynistic content is pervasive on some Reddit communities, but a training dataset for misogorical classification has not been created with the data. |
| Approach: | They propose a hierarchical taxonomy and an expert labelled dataset to enable automatic classification of online misogynistic content. |
| Outcome: | The proposed taxonomy and an expert labelled dataset are made freely available for future research. |
“Fifty Shades of Bias”: Normative Ratings of Gender Bias in GPT Generated English Text (2023.emnlp-main)
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| Challenge: | Prior work treats gender bias as a binary classification task, but a comparative annotation framework can be used to assess the impact of biases. |
| Approach: | They propose to generate a dataset with normative ratings of gender bias in English text with a comparative annotation framework. |
| Outcome: | The first dataset of GPT-generated English text with normative ratings of gender bias is analyzed using Best–Worst Scaling . |
WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context (2021.eacl-main)
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| Challenge: | Existing benchmarks for Word Sense Disambiguation are limited to those systems in which sense distinctions are defined according to an underlying sense inventory. |
| Approach: | They propose a framework for Target Sense Verification of Words in Context which grounds its uniqueness as binary classification task and independent of external sense inventories. |
| Outcome: | The proposed framework is highly flexible for evaluation of diverse models and systems in and across domains. |
BullyBench: Youth & Experts-in-the-loop Framework for Intrinsic and Extrinsic Cyberbullying NLP Benchmarking (2025.emnlp-industry)
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Kanishk Verma, Sri Balaaji, Joachim Wagner, Arefeh Kazemi, Darragh Mccashin, Isobel Walsh@dcu, Sayani Basak, Sinan Asci, Yelena Cherkasova, Alexandros Poulis, James Ohiggins Norman, Rebecca Umbach Umbach, Tijana Milosevic, Brian Davis
| Challenge: | Existing youth-focused CB datasets lack conversational realism and ethical youth involvement with little or no evaluation of their social plausibility. |
| Approach: | They propose a youth-in-the-loop dataset “BullyBench” that incorporates a structured intrinsic quality evaluation with experts-in the-looop (social scientists, psychologists, and content moderators) they perform extrinsic baseline evaluation by benchmarking encoder- and decoder-only language models for multi-class CB role classification. |
| Outcome: | The proposed dataset is evaluated by a team of social scientists, psychologists, and content moderators to assess its quality, relevance, and coherence. |
Beyond Binary: Towards Embracing Complexities in Cyberbullying Detection and Intervention - a Position Paper (2024.lrec-main)
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Kanishk Verma, Kolawole John Adebayo, Joachim Wagner, Megan Reynolds, Rebecca Umbach, Tijana Milosevic, Brian Davis
| Challenge: | Existing methods for CB detection oversimplify the problem of CB as a binary classification task. |
| Approach: | They propose to use large language models to generate CB-related datasets . they propose to combine cognitive and linguistic models to help identify CB incidents . |
| Outcome: | The proposed approach aims to help researchers and policymakers make informed decisions . it uses large language models such as Claude-2 and Llama2-Chat to generate CB-related datasets . |
CoRoSeOf - An Annotated Corpus of Romanian Sexist and Offensive Tweets (2022.lrec-1)
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| Challenge: | Using CoRoSeOf, we manually annotate social media for sexist and offensive language. |
| Approach: | They introduce a large corpus of Romanian social media manually annotated for sexist and offensive language. |
| Outcome: | The proposed corpus contains 39 245 tweets annotated by multiple annotators with an agreement rate of Fleiss’= 0.45 . |
Scalar Adjective Identification and Multilingual Ranking (2021.naacl-main)
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| Challenge: | Existing studies on scalar adjective ranking have focused on English due to the availability of datasets for evaluation. |
| Approach: | They propose a binary classification task to examine the models’ ability to distinguish scalar from relational adjectives in English. |
| Outcome: | The proposed task compares the models' ability to distinguish scalar from relational adjectives in English using monolingual and multilingual models. |
Elvis vs. M. Jackson: Who has More Albums? Classification and Identification of Elements in Comparative Questions (2022.lrec-1)
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| Challenge: | Comparative Question Answering (cQA) is the task of providing accurate answers to questions . most question answering systems focus on answering factoid questions, but they fail at answering comparative questions in an efficient argumentative manner. |
| Approach: | They propose two new open-domain datasets for identifying and labeling comparative questions . they use a binary classification task and an unsupervised sequence labeling task . |
| Outcome: | The proposed datasets reach close-to-human results on a binary classification task with a neural model using ALBERT embeddings. |
AcrosticSleuth: Probabilistic Identification and Ranking of Acrostics in Multilingual Corpora (2025.findings-naacl)
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| Challenge: | acrostics are hidden messages in which initial letters of consecutive lines or paragraphs form meaningful words or phrases. |
| Approach: | They propose a method to identify acrostics automatically and rank them by the probability that the corresponding sequence of characters does not occur by chance. |
| Outcome: | The proposed method achieves F1 scores of 0.39, 0.59, and 0.66 on the French, English, and Russian subdomains of WikiSource. |
Similarity or deeper understanding? Analyzing the TED-Q dataset of evoked questions (2020.coling-main)
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| Challenge: | TED-Q datasets are annotated with the questions they implicitly evoke, based on a dataset of TED talks . we test whether relation between a discourse and questions it evokes is one of similarity or association . |
| Approach: | They construct a binary classification task from TED-Q and fit a BERT-based classifier alongside models based on different notions of similarity. |
| Outcome: | The proposed classifier outperforms similarity-based models in the TED-Q dataset. |
ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation (2022.emnlp-main)
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| Challenge: | Existing datasets only annotate a binary label for each sentence pair. Existing models only annnotate binary labels for each phrase pair. |
| Approach: | They propose a novel binary paraphrase classification task that annotates the degree of paraphrase between sentences and a new annotation schema that labels the minimum spans of tokens in a sentence that don't have the corresponding paraphrases in the other sentence. |
| Outcome: | The proposed dataset can be used to train an automatic scorer for language generation evaluation. |
Machine-Generated Text Localization (2024.findings-acl)
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| Challenge: | Prior work focused on identifying only part of a document as machine or human written . a key challenge is that short spans of text provide little information indicating if it is machine generated due to its short length . |
| Approach: | They propose a method that localizes the portions of a document that were machine generated. |
| Outcome: | The proposed method can detect changes in style or content to boost performance. |
Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking (2025.coling-main)
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| Challenge: | Claim matching (CM) is a binary classification task that can be used to determine if two claims can be verified using the same piece of evidence or fact-check. |
| Approach: | They propose a claim matching task that uses binary classification and large language models to test out learning approaches to the task. |
| Outcome: | The proposed task can be tackled by leveraging mature tasks such as natural language inference or paraphrase detection. |
Improving Bias Mitigation through Bias Experts in Natural Language Understanding (2023.emnlp-main)
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| Challenge: | Existing approaches to mitigate the detrimental effect of bias on the network include debiasing methods that down-weight the biased examples identified by an auxiliary model, which is trained with explicit bias labels. |
| Approach: | They propose a framework that introduces binary classifiers between the auxiliary model and main model, coined bias experts, to reduce the detrimental effect of bias on the network. |
| Outcome: | The proposed approach outperforms the state-of-the-art on various datasets while achieving high performance on in-distribution data. |
Leveraging Hashtag Networks for Multimodal Popularity Prediction of Instagram Posts (2022.lrec-1)
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| Challenge: | Existing popularity prediction approaches reduce hashtags to simple features such as hashtag length or number of hashtags in a post. |
| Approach: | They propose a multimodal framework to predict popular influencer posts on Instagram using post captions, image, hashtag network and topic model. |
| Outcome: | The proposed framework outperforms baseline models and unimodal models on popular influencer posts in Taiwan . it uses post captions, image, hashtag network, and topic model to predict popular influence post . |
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups (2024.emnlp-main)
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Răzvan-Alexandru Smădu, David-Gabriel Ion, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel
| Challenge: | Large language models (LLMs) are popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings. |
| Approach: | They investigate the use of large language models in CWI, LCP, and MWE settings by evaluating their use in zero-shot, few-shot and fine-tuning settings. |
| Outcome: | The proposed models struggle in certain conditions or achieve comparable results against existing methods. |
The Corpus AIKIA: Using Ranking Annotation for Offensive Language Detection in Modern Greek (2024.lrec-main)
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Stella Markantonatou, Vivian Stamou, Christina Christodoulou, Georgia Apostolopoulou, Antonis Balas, George Ioannakis
| Challenge: | OLD is a less-resourced language regarding OLD. |
| Approach: | They propose to annotate OLD in Modern Greek using the lexicon of offensive terms that originates from HurtLex. |
| Outcome: | The proposed corpus is based on the lexicon of offensive terms that originates from HurtLex and can be used to detect offensive language in modern Greek. |
ToNER: Type-oriented Named Entity Recognition with Generative Language Model (2024.lrec-main)
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| Challenge: | Input too many potential entity types would distract the model inevitably. |
| Approach: | They propose to use a generative model to exploit entity types' merit on promoting NER task by appending a type matching model to identify the entity types most likely to appear in the sentence. |
| Outcome: | The proposed framework exploits entity types' merit on promoting NER task by adding auxiliary task to the model to discover the entity types. |
Transfer Fine-tuning for Quality Estimation of Text Simplification (2024.lrec-main)
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| Challenge: | Experimental results show that quality estimation of text simplification models can be improved on a small labeled corpus. |
| Approach: | They propose a method to train quality estimation of text simplification on a small-scale labeled corpus prior to fine-tuning pre-trained language models. |
| Outcome: | The proposed method improves quality estimation of text simplification on a small-scale labeled corpus. |
What Can Diachronic Contexts and Topics Tell Us about the Present-Day Compositionality of English Noun Compounds? (2024.lrec-main)
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| Challenge: | Existing methods to determine the semantic relatedness between compounds and constituents have applied a synchronic perspective, but this study examines what diachronic changes in contexts and semantic topics reveal about the compounds’ present-day compositionality. |
| Approach: | They propose to use two diachronic vector spaces to model compositional patterns between compounds with low and high present-day compositionality. |
| Outcome: | The proposed model performs on par with co-occurrence space and captures similar information. |