Papers with machine learning approaches
The PRECOM-SM Corpus: Gambling in Spanish Social Media (2025.coling-main)
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| Challenge: | ESTUDES de Sanidad: 20.1% of youngsters between 14 and 18 years old have gambled money online . ESTUDES: 2021: 17.9% of students who have gamble would be predisposed to gambling-related problems. |
| Approach: | This paper collects text from online Spanish-speaking communities and analyses it to detect gambling addiction problem. |
| Outcome: | The proposed corpus collects text from Spanish-speaking communities and analyzes it . it finds patterns in written language from frequent and infrequent users . 20.1% of youngsters between 14 and 18 years old have gambled money in person or online . |
Automatic identification of writers’ intentions: Comparing different methods for predicting relationship goals in online dating profile texts (D19-55)
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| Challenge: | lexicon-based text analysis methods such as LIWC have been criticized by computational linguists for their lack of adaptability, but they have not been systematically compared with either human evaluations or machine learning approaches. |
| Approach: | They used a corpus of online dating profile texts to compare LIWC, machine learning, and a human baseline to assess their effectiveness on a relationship goal classification task. |
| Outcome: | The proposed methods were compared with a corpus of online dating profile texts and a human baseline. |
Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale? (2025.coling-industry)
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| Challenge: | Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification, but the vast number of labels can exceed LLMs’ input limits. |
| Approach: | They propose a method that integrates large language models with dense retrieval techniques to overcome these challenges. |
| Outcome: | The proposed methods avoid frequent retraining by leveraging zero-shot and few-shot learning for real-time label assignment. |
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols (N18-2)
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| Challenge: | Existing efforts to annotate natural language instructions into machine-readable formats are limited. |
| Approach: | They propose to annotate a corpus of natural language instructions consisting of 622 wet lab protocols to facilitate automatic or semi-automatic conversion into a machine-readable format. |
| Outcome: | The proposed corpus can be used to facilitate automatic or semi-automatic conversion of protocols into a machine-readable format and benefit biological research. |
Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature (D19-57)
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| Challenge: | BB system is among the top two systems in five of all six subtasks . knowledge about microbial diversity is crucial for the study of microbiome and bacteria . |
| Approach: | They present a system that uses word embedding and lexical features to perform entities recognition, normalization and relation extraction. |
| Outcome: | The proposed system achieves state-of-the-art in five of six subtasks and is among the top two in five. |
Discourse-Aware In-Context Learning for Temporal Expression Normalization (2024.naacl-short)
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| Challenge: | Temporal expression (TE) normalization is a well-studied problem, but upcoming machine learning approaches suffer from a lack of labeled data. |
| Approach: | They propose to use in-context learning to inject task, document, and example information into a large language model for temporal expression normalization. |
| Outcome: | The proposed model performs better in non-standard settings by dynamically including relevant examples during inference. |
Boosting Transformers and Language Models for Clinical Prediction in Immunotherapy (2023.acl-industry)
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| Challenge: | Current machine learning approaches to predict clinical outcomes are limited to tabular data and are not applicable to clinical prediction. |
| Approach: | They investigate the potential of transformers to improve clinical prediction compared to conventional machine learning approaches and address the challenge of few-shot learning in predicting rare disease areas. |
| Outcome: | The proposed model improves the accuracy of baseline models and language models under few-shot regimes and shows that it is more accurate than previous models. |
Fact or Factitious? Contextualized Opinion Spam Detection (P19-2)
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| Challenge: | In analytic analysis of fake reviews, we compare a number of techniques to detect them. |
| Approach: | They propose a machine learning approach that fine-tunes contextualised embeddings to detect fake reviews. |
| Outcome: | The proposed approach fine-tunes state of the art contextualised embeddings to show that it is effective in detecting fake reviews and lay the groundwork for future research in this area. |
Claim Extraction and Law Matching for COVID-19-related Legislation (2022.lrec-1)
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| Challenge: | Existing approaches to extract legal claims from news articles and match them with applicable laws are difficult for laypersons to learn since news articles do not refer to underlying laws. |
| Approach: | They propose an automated approach to extract legal claims from news articles and match the claims with applicable laws. |
| Outcome: | The proposed model achieves 46.7 F1 for claim extraction and 91.4 F1 law matching, despite conceptual limitations. |
Understanding User Utterances in a Dialog System for Caregiving (2020.lrec-1)
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| Challenge: | a dialog system that can monitor the health status of seniors has a huge potential for solving the labor shortage in the caregiving industry in aging societies. |
| Approach: | They are developing a yes/no response classifier and an entailment recognizer to correctly interpret user utterances. |
| Outcome: | The proposed system can correctly interpret user utterances and can monitor the health of seniors. |
Exploring Amharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models (2020.coling-main)
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| Challenge: | Existing crowdsourcing platforms do not support sentiment analysis for Amharic, and there are no expert researchers in the area. |
| Approach: | They propose to build a social-network-friendly Amharic sentiment analysis tool using the Telegram bot and collect 9.4k tweets where each tweet is annotated by three Telegram users. |
| Outcome: | The proposed system outperforms existing classifiers in Amharic and other low-resource languages due to the widespread use of sarcasm and figurative speech. |
Rational Recurrences (D18-1)
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| Challenge: | Recent studies show that neural models lack strong intuitions . recent studies show connections between convolutional neural networks and weighted finite state automata (WFSAs) |
| Approach: | They show that some recurrent neural networks share a connection to weighted finite state automata (WFSAs) they define rational recurrences as recursive hidden state update functions . they propose to use these functions to write forward calculations of a finite set of WFSA's . |
| Outcome: | The proposed model outperforms two baselines on language modeling and text classification. |
A Deep Generative Approach to Native Language Identification (2020.coling-main)
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| Challenge: | Native language identification (NLI) is a multi-class classification task involving multiple features that capture the systematic fingerprints of the first language in the second language writing. |
| Approach: | They propose a deep generative language modelling approach to NLI that fine-tunes a GPT-2 model separately on texts written by the authors with the same L1 and assigns n-grams to an unseen text. |
| Outcome: | The proposed method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets. |
CXR Data Annotation and Classification with Pre-trained Language Models (2022.coling-1)
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Nina Zhou, Ai Ti Aw, Zhuo Han Liu, Cher heng Tan, Yonghan Ting, Wen Xiang Chen, Jordan sim zheng Ting
| Challenge: | Existing tools for clinical data annotation are limited to specific institutions due to differences in writing style, structure, language use and label definition. |
| Approach: | They propose a weak supervision annotation framework with two improvements over existing ones . the framework provides an efficient form of sample selection and data auto-annotation . |
| Outcome: | The proposed framework provides better results for clinical data annotation tasks compared to existing frameworks. |
How Lexical is Bilingual Lexicon Induction? (2024.findings-naacl)
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| Challenge: | lexical variation and low-resource settings make it difficult to learn in low-level settings. |
| Approach: | They propose to incorporate additional lexical information into the retrieve-and-rank approach to improve lexicon induction. |
| Outcome: | The proposed approach improves on XLING by an average of 2% across all language pairs. |
Constructing Web-Accessible Semantic Role Labels and Frames for Japanese as Additions to the NPCMJ Parsed Corpus (2020.lrec-1)
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| Challenge: | Adding semantic role labels to the NPCMJ will help language learners and linguists search for syntactic and semantic features. |
| Approach: | They propose to add frame information for predicates and two types of semantic role labels that mark contributions of arguments to the NINJAL Parsed Corpus of Modern Japanese (NPCMJ) this will provide a web-accessible language resource for linguists and language learners searching for syntactic and semantic features. |
| Outcome: | The proposed framework will be able to search examples of Japanese for syntactic and semantic features. |
Active Learning for Rumor Identification on Social Media (2021.findings-emnlp)
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| Challenge: | Existing methods for rumor tracking depend on a significant amount of labeled data. |
| Approach: | They propose an Active-Transfer Learning strategy to identify rumors with limited amount of annotated data. |
| Outcome: | The proposed approach achieves faster convergence in terms of the F-score while requiring fewer annotated samples (42% of the whole dataset for the best model). |
Offensive language detection in Hebrew: can other languages help? (2022.lrec-1)
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| Challenge: | Various approaches for offensive language detection have been applied for this task . contamination of social networks with offensive content is a new reality affecting almost all of us . |
| Approach: | They propose to use multiple supervised models and text representations to detect offensive language in three languages, including two Semitic languages. |
| Outcome: | The proposed model can detect offensive content in two Semitic languages, including Hebrew and Arabic, and it is able to perform cross-lingual and multilingual learning. |
Peeking inside the black box: A Commonsense-aware Generative Framework for Explainable Complaint Detection (2023.acl-long)
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| Challenge: | Complaining is an expression of negative emotions communicated due to a discrepancy between reality and expectations. |
| Approach: | They propose to use an explainable complaint dataset to generate a commonsense-aware generative framework that can predict the complaint cause, severity level, emotion, and polarity of the text. |
| Outcome: | The proposed model can predict the complaint cause, severity level, emotion, and polarity of the text in addition to detecting whether it is a complaint or not. |
Building a Dataset for Automatically Learning to Detect Questions Requiring Clarification (2022.lrec-1)
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| Challenge: | Existing work on question answering systems assumes all questions are intelligible and unambiguous . however, available datasets do not meet requirements for building commercial virtual assistants . |
| Approach: | They propose to make question answering systems more robust by classifying if question is intelligible and returning a clarification question for contextual ambiguity. |
| Outcome: | The proposed system can classify if the input question is intelligible and return a clarification question for ambiguous questions. |
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification (2020.acl-main)
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| Challenge: | Existing approaches to comparative preference classification do not learn entity-aware representations well or use sequential modeling approaches that do not generalize well. |
| Approach: | They propose a deep-level deep-graph attention network that leverages word embeddings and syntactic information to solve a comparative preference classification problem. |
| Outcome: | The proposed model achieves state-of-the-art performance in comparative preference classification. |
T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples (L18-1)
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Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, Elena Simperl
| Challenge: | Existing datasets that provide alignments between natural language and knowledge bases (KB) triples are limited in size, lack coverage and are of unreported quality. |
| Approach: | They propose to build a large scale dataset of alignments between Wikipedia abstracts and Wikidata triples that is two orders of magnitude larger than the largest available alignments dataset. |
| Outcome: | The proposed dataset is two orders of magnitude larger than the largest available dataset and covers 2.5 times more predicates. |
Fair Without Leveling Down: A New Intersectional Fairness Definition (2023.emnlp-main)
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| Challenge: | Existing approaches to capture intersectional group fairness lack significant unfairness at intersection levels. |
| Approach: | They propose a new definition of intersectional fairness that combines absolute and relative performance across sensitive groups. |
| Outcome: | The proposed definition does not improve on a simple baseline. |
Developing A Multilabel Corpus for the Quality Assessment of Online Political Talk (2022.lrec-1)
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| Challenge: | a corpus of political tweets labeled for its deliberative characteristics is presented . the dataset offers a first step in building dictionaries to aid in the measurement of the Discourse Quality Index . |
| Approach: | They present a Twitter Deliberative Politics dataset that measures the quality of political tweets . they propose to use machine learning to analyze tweets and to use it to build dictionaries . |
| Outcome: | The proposed dataset is useful to linguists, political scientists, and social scientists . it offers a first step in building dictionaries for the quality assessment of political talk in english . |
Evaluation of Manual and Non-manual Components for Sign Language Recognition (2020.lrec-1)
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Medet Mukushev, Arman Sabyrov, Alfarabi Imashev, Kenessary Koishybay, Vadim Kimmelman, Anara Sandygulova
| Challenge: | Deaf communities communicate via sign languages to express meaning and intent. |
| Approach: | They used sign samples from 20 commonly used signs in Kazakh-Russian Sign Language (K-RSL) to investigate whether non-manual components would improve sign’s recognition accuracy. |
| Outcome: | The results showed that using non-manual components would improve sign recognition accuracy. |
A Federated Approach to Predicting Emojis in Hindi Tweets (2022.emnlp-main)
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| Challenge: | emojis are a visual modality to, often private, textual communication, but their use tends to cluster into the frequently used and the rarely used eojis. |
| Approach: | They propose to use 118k tweets to predict emojis in Hindi and a federated learning algorithm to achieve a balance between model performance and user privacy. |
| Outcome: | The proposed approach achieves comparative scores with more complex centralised models while minimising risks to user privacy. |
Data Query Language and Corpus Tools for Slot-Filling and Intent Classification Data (2020.lrec-1)
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| Challenge: | Typical machine learning approaches require large amounts of training data . Managing training data can be cumbersome without dedicated tools . |
| Approach: | They propose a toolkit for analyzing slot-filling and intent classification corpora . they propose 'Query Language' for searching such corporan and tools for understanding structure . |
| Outcome: | The proposed toolkit can be used to uncover interesting and surprising insights. |
So Hateful! Building a Multi-Label Hate Speech Annotated Arabic Dataset (2024.lrec-main)
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| Challenge: | Social media enables widespread propagation of hate speech targeting groups based on ethnicity, religion, or other characteristics. |
| Approach: | They analyze 70,000 Arabic tweets to identify hate speech patterns and train models . 15% of tweets contain offensive language while 6% have hate speech . authors hope to prevent spread of hateful content on social media platforms . |
| Outcome: | The analysis of 70,000 Arabic tweets shows that 15% of tweets contain offensive language while 6% have hate speech . 10% of tweet provide verifiable factual claims, and 7% are deemed important . |
Assay2Mol: Large Language Model-based Drug Design Using BioAssay Context (2025.emnlp-main)
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| Challenge: | Unstructured text that describes biological mechanisms of assays is untapped for early-stage drug discovery. |
| Approach: | They propose a large language model-based workflow that can capitalize on existing biochemical screening assays for early-stage drug discovery. |
| Outcome: | Assay2Mol outperforms machine learning approaches that generate candidate compounds for protein structures while promoting more synthesizable molecule generation. |
CompassLLM: A Multi-Agent Approach toward Geo-Spatial Reasoning for Popular Path Query (2026.findings-acl)
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| Challenge: | Existing algorithms and machine learning methods require model training, parameter tuning, and retraining when accommodating data updates. |
| Approach: | They propose a multi-agent framework that leverages the reasoning capabilities of Large Language Models into the geo-spatial domain to solve the popular path query. |
| Outcome: | Experiments on real and synthetic datasets show that CompassLLM performs better than existing models while being cost-effective. |
CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have enabled more sophisticated content moderation, but these methods lack generalization, interpretability, and adaptability to unseen or ambiguous cases. |
| Approach: | They propose a new moderation framework that leverages analogical examples to enhance rule induction and decision reliability. |
| Outcome: | The proposed method outperforms rule-injected fine-tuning baselines and multi-stage static RAG pipelines in terms of moderation accuracy and rule quality. |
Statistical and Neural Methods for Hawaiian Orthography Modernization (2025.emnlp-main)
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| Challenge: | Hawaiian orthography employs two distinct spelling systems, both of which are used by communities of speakers today. |
| Approach: | They develop models that convert between the ‘okina letter and kahak diacritic, which represent glottal stops and long vowels, respectively. |
| Outcome: | The proposed models outperform neural seq2seq models and LLMs in a low-resource setting, highlighting the potential for traditional machine learning approaches in . low-cost environments. |