Papers with Machine Learning
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (2023.eacl-main)
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| Challenge: | EACL 2023 submissions were divided into 21 areas . the areas were "Generation and Summarization", "Language Resources and Evaluation" and "Machine Learning in NLP". |
| Approach: | EACL 2023 submissions were divided into 21 areas . most popular areas were "Generation and Summarization", "Language Resources and Evaluation" and "Machine Learning in NLP" |
| Outcome: | EACL 2023 submissions were divided into 21 areas . the areas most popular with over 100 submissions included "Generation and Summarization", "Language Resources and Evaluation" |
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2023.acl-long)
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| Challenge: | a program chair's job is to help reviewers and reviewers make better decisions . 80% of reviewers, reviewers voted to change the criteria for soundness and excitement . |
| Approach: | a new process for matching papers to reviewers based on keywords was proposed . the authors have also brought back miniconf and RocketChat to allow for better virtual communication . |
| Outcome: | a new process for matching papers to reviewers based on keywords allowed more fine-grained control over the paper-reviewer matches. |
Findings of the Association for Computational Linguistics: ACL 2023 (2023.findings-acl)
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| Challenge: | a program chair's job is to help reviewers and reviewers make better decisions . 80% of reviewers, reviewers voted to change the criteria for soundness and excitement . |
| Approach: | a new process for matching papers to reviewers based on keywords was proposed . the authors have also brought back miniconf and RocketChat to allow for better virtual communication . |
| Outcome: | a new process for matching papers to reviewers based on keywords allowed more fine-grained control over the paper-reviewer matches. |
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (2023.acl-short)
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| Challenge: | a program chair's job is to help reviewers and reviewers make better decisions . 80% of reviewers, reviewers voted to change the criteria for soundness and excitement . |
| Approach: | a new process for matching papers to reviewers based on keywords was proposed . the authors have also brought back miniconf and RocketChat to allow for better virtual communication . |
| Outcome: | a new process for matching papers to reviewers based on keywords allowed more fine-grained control over the paper-reviewer matches. |
Distributed Knowledge Based Clinical Auto-Coding System (P19-2)
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| Challenge: | Codification of free-text clinical narratives has long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. |
| Approach: | They propose to use NLP and related machine learning techniques to assign ICD-10-AM and ACHI codes to clinical records using local and international standards. |
| Outcome: | The proposed system utilises NLP and ML techniques to assign ICD-10-AM and ACHI codes to clinical records while adhering to local and international standards. |
An Embarrassingly Simple Approach for Intellectual Property Rights Protection on Recurrent Neural Networks (2022.aacl-main)
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| Challenge: | Existing protection schemes for deep neural network models protect intellectual property rights from being abused, stolen and plagiarized. |
| Approach: | They propose a practical approach for the IPR protection on recurrent neural networks without all the bells and whistles of existing IPR solutions. |
| Outcome: | The proposed approach is robust and effective against ambiguity and removal attacks on different RNN variants. |
EDAR: A pipeline for Emotion and Dialogue Act Recognition (2025.coling-industry)
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| Challenge: | EDAR is a pipeline for Emotion and Dialogue Act Recognition for debt collection . traditional methods overlook the emotional complexities of debtors, leading to increased stress for both parties. |
| Approach: | They propose to integrate EDAR into decision-making systems to improve debt collection outcomes. |
| Outcome: | The proposed pipeline improves debt collection outcomes and debtor satisfaction by identifying emotional states and enabling empathetic responses. |
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing (N18-1)
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| Challenge: | Currently, machine learning is limited in scalability and is limited to specific training data. |
| Approach: | They propose to enhance learning models with world knowledge in the form of Knowledge Graph fact triples for natural language processing tasks. |
| Outcome: | The proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. |
Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms (2025.acl-long)
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| Challenge: | Social media platforms use machine learning and artificial intelligence to maximize user engagement, but can indirectly cause exposure to harmful content. |
| Approach: | They propose a re-ranking approach using Large Language Models to assess and rerank content sequences using large annotated data sets. |
| Outcome: | The proposed method significantly outperforms existing proprietary moderation methods on three datasets, three models and across three configurations. |
TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation (P18-1)
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Alexander Fabbri, Irene Li, Prawat Trairatvorakul, Yijiao He, Weitai Ting, Robert Tung, Caitlin Westerfield, Dragomir Radev
| Challenge: | TutorialBank is a publicly available dataset that aims to facilitate NLP education and research . a google search of "Natural Language Processing" returns over 100 million hits with papers, tutorials, 1 http://aan.how blog posts, codebases and other related online resources. |
| Approach: | They have manually collected and categorized over 5,600 resources on NLP . they have created a search engine and command-line tool to search the corpus . |
| Outcome: | The tutorial bank dataset is the largest manually-picked corpus of resources intended for NLP education . it includes lists of research topics, relevant resources for each topic, prerequisite relations among topics . |
ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)
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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong
| Challenge: | Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs). |
| Approach: | They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models. |
| Outcome: | The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks. |
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models (2021.emnlp-main)
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| Challenge: | Existing approaches to generate relevant Knowledge Bases from text and graph data are gaining popularity. |
| Approach: | They propose a bidirectional generation of text and graph leveraging Reinforcement Learning. |
| Outcome: | The proposed system improves on WebNLG+ 2020 and TekGen datasets. |
Word Embedding Binarization with Semantic Information Preservation (2020.coling-main)
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| Challenge: | Word and Sentence embeddings are one of the most common starting points of any NLP task. |
| Approach: | They propose a way to convert word embedding to binary representation to reduce overall size . they propose different approaches suitable for different downstream tasks based on contextual and semantic information. |
| Outcome: | The proposed method reduces the size of the embedding while keeping the semantic and relational knowledge intact. |
Are Text Classifiers Xenophobic? A Country-Oriented Bias Detection Method with Least Confounding Variables (2024.lrec-main)
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| Challenge: | Existing methods for detecting biases are biased because of confounding variables . authors propose a method to detect the biased classifier on any type of unlabeled data . |
| Approach: | They propose a method to detect biases of a specific fine-tuned classifier on unlabeled data. |
| Outcome: | The proposed method detects biases on unlabeled data on named entity perturbations . it uses name-entity recognition on target-domain data and morphosynctactically different languages spoken in relation to countries of the target groups . |
Exploring hybrid approaches to readability: experiments on the complementarity between linguistic features and transformers (2024.findings-eacl)
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| Challenge: | Linguistic features have been a key component of the automatic assessment of text readability (ARA) with the development in the ARA field, the research moved to Deep Learning (DL) |
| Approach: | They compare 6 hybrid approaches to Machine Learning and DL on 4 corpora and found they are the most robust on smaller datasets and across languages. |
| Outcome: | The proposed approaches perform better on smaller datasets and across languages and tasks. |
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
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Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent . |
| Approach: | They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods. |
| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
Federated Learning with Noisy User Feedback (2022.naacl-main)
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Rahul Sharma, Anil Ramakrishna, Ansel MacLaughlin, Anna Rumshisky, Jimit Majmudar, Clement Chung, Salman Avestimehr, Rahul Gupta
| Challenge: | Artificial Intelligence (AI) and Machine Learning (ML) systems are becoming more popular and are causing concerns over user privacy. |
| Approach: | They propose a method for training ML models using positive and negative user feedback and a framework to extract labels on edge to make FL viable. |
| Outcome: | The proposed method improves significantly over a self-training baseline, achieving performance closer to models trained with full supervision. |
RESF: Regularized-Entropy-Sensitive Fingerprinting for Black-Box Tamper Detection of Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods for tamper detection rely on model stability, not inherently stochastic models. |
| Approach: | They propose a hypothesis-testing method for black-box tamper detection for LLMs . they propose regularized entropy-sensitive fingerprinting to enable efficient fingerprinting . |
| Outcome: | The proposed method achieves 98.80% detection accuracy under challenging conditions . it is based on a first-order surrogate for KL divergence to identify prompts most responsive to parameter perturbations. |
Causal Intersectionality and Dual Form of Gradient Descent for Multimodal Analysis: A Case Study on Hateful Memes (2024.lrec-main)
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| Challenge: | Causal analyses define semantics, while gradient-based methods are essential to eXplainable AI (XAI), interpreting the modelās āblack boxā. |
| Approach: | They propose to integrate causal analysis and XAI to integrate a model's mechanisms into their analysis by integrating a dataset of hateful meme detection models. |
| Outcome: | The proposed model can detect hateful memes using intersectionality principles and summarized attention scores highlight distinct behaviors of three Transformer models. |
Experiments with Convolutional Neural Networks for Multi-Label Authorship Attribution (L18-1)
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| Challenge: | Existing methods for authorship attribution tasks are difficult, but they are effective. |
| Approach: | They propose a CNN that averaging author probability distributions at sentence level for longer documents and treating smaller documents as sentences adapts to single-label datasets and various document sizes. |
| Outcome: | The proposed method outperforms state-of-the-art models on a single-label AA benchmark dataset. |
CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification (2024.findings-acl)
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| Challenge: | Contaminated or adulterated food poses a substantial risk to human health. |
| Approach: | They present a dataset of 7,546 text messages describing public food recalls. |
| Outcome: | The proposed model outperforms RoBERTa and XLM-R on classes with low support while reducing energy consumption. |
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks (2023.acl-long)
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| Challenge: | Existing text adversarial attacks are impractical in real-world scenarios where humans are involved. |
| Approach: | They have surveyed 378 human participants about the perceptibility of text adversarial examples produced by state-of-the-art methods. |
| Outcome: | The proposed methods ignore the property of imperceptibility or study it under limited conditions. |
Empowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings (2024.lrec-main)
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| Challenge: | Existing approaches to augment LLMs with Knowledge Graphs (KGs) Knowledge-intensive tasks are prone to errors and require a large amount of knowledge to be understood. |
| Approach: | They propose a framework for augmenting LLMs through Knowledge Graphs (KGs) they propose KGs can be used to enhance performance in knowledge-intensive tasks . |
| Outcome: | Experimental results show that a small domain-specific KG can benefit from a performance boost in downstream tasks when linked to a substantial general-purpose KG. |
Natural Language Processing for Achieving Sustainable Development: the Case of Neural Labelling to Enhance Community Profiling (2020.emnlp-main)
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| Challenge: | In recent years, there has been an increasing interest in the application of Artificial Intelligence (AI) to the field of Sustainable Development (SD). |
| Approach: | They propose a new extreme multi-class multi-label Automatic UserPerceived Value classification task that uses a complex corpus of interviews to investigate the problem. |
| Outcome: | The proposed task solves a cost- and time-barrier in constructing qualitative data that prevents its widespread use and associated benefits. |
SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories (2024.emnlp-main)
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Ben Bogin, Kejuan Yang, Shashank Gupta, Kyle Richardson, Erin Bransom, Peter Clark, Ashish Sabharwal, Tushar Khot
| Challenge: | Large Language Models (LLMs) have made significant progress in writing code, but can they be used to reproduce results from research repositories? |
| Approach: | They propose a benchmark to evaluate the capability of Large Language Models to reproduce results from research repositories. |
| Outcome: | The benchmark aims to capture the realistic challenges faced by researchers working with machine learning and natural language processing repositories. |
Harnessing Large Language Models as Post-hoc Correctors (2024.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated their effectiveness in a wide range of tasks, including machine translation and commonsense reasoning. |
| Approach: | They propose a training-free framework that can work as a post-hoc corrector to propose corrections for ML models. |
| Outcome: | The proposed framework improves the performance of a number of models by up to 39% on text analysis and the challenging molecular predictions. |
Puntuguese: A Corpus of Puns in Portuguese with Micro-edits (2024.lrec-main)
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Marcio Lima Inacio, Gabriela Wick-Pedro, Renata Ramisch, LuĆs EspĆrito Santo, Xiomara S. Q. Chacon, Roney Santos, RogĆ©rio Sousa, Rafael AnchiĆŖta, Hugo Goncalo Oliveira
| Challenge: | Existing corpus of punning humor in Portuguese is unfit for machine learning due to data leakage. |
| Approach: | They propose to use Puntuguese to create a corpus of punning humor in Portuguese that is significantly more difficult to recognize than the previous corpus. |
| Outcome: | The proposed corpus achieves an F1-Score of 68.9% and is significantly more difficult than the previous corpus. |
Research Community Perspectives on āIntelligenceā and Large Language Models (2025.findings-acl)
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| Challenge: | Despite the widespread use of āartificial intelligenceā (AI) framing in NLP research, it is not clear what researchers mean by āintelligenceā. |
| Approach: | They propose to use the term "AI" to describe the perception of a system as intelligent, but note that it is not accepted by the majority of respondents. |
| Outcome: | The results suggest that the perception of the current NLP systems as 'intelligent' is a minority position (29%). |
FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data (2026.acl-long)
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| Challenge: | Social media text data is often used to train machine learning models to identify users exhibiting high-risk mental health behaviors. |
| Approach: | They apply federatedlearning and Differentially Private FL to two widely-studied mental health prediction tasks using social media text data. |
| Outcome: | The proposed methods achieve comparable performance to centralized training on depression identification, but have a large performance-privacy trade-off even with low levels of noise. |