Papers with data processing
NeuroX Library for Neuron Analysis of Deep NLP Models (2023.acl-demo)
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| Challenge: | NeuroX is an open-source toolkit to conduct neuron analysis of natural language processing models. |
| Approach: | They propose a Python toolkit to conduct neuron analysis of natural language processing models. |
| Outcome: | a new open-source toolkit enables neuron analysis of natural language processing models . the framework provides a framework for data processing and evaluation, making it easier for researchers and practitioners to perform neuron analyses. |
CAPID: Context-Aware PII Detection for Question-Answering Systems (2026.eacl-srw)
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Mariia Ponomarenko, Sepideh Abedini, Masoumeh Shafieinejad, D. B. Emerson, Shubhankar Mohapatra, Xi He
| Challenge: | Existing approaches mainly redact all PII, disregarding the fact that some may be contextually relevant to the user’s question, resulting in a degradation of response quality. |
| Approach: | They propose a method that fine-tunes a locally owned small language model that filters sensitive information before it is passed to LLMs for QA. |
| Outcome: | The proposed approach outperforms baselines in span, relevance and type accuracy while preserving significantly higher utility under anonymization. |
SPY: Enhancing Privacy with Synthetic PII Detection Dataset (2025.naacl-srw)
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| Challenge: | Historically, Named Entity Recognition (NER) has been employed for PII detection, but PI I entities constitute a subset of NER entities. |
| Approach: | They propose to use Large Language Models to generate a synthetic dataset that emulates real-world PII scenarios and validate its quality. |
| Outcome: | The proposed dataset is validated and provides a benchmark for PII detection. |
StarFlow: Generating Structured Workflow Outputs From Sketch Images (2026.eacl-long)
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Patrice Bechard, Chao Wang, Amirhossein Abaskohi, Juan A. Rodriguez, Christopher Pal, David Vazquez, Spandana Gella, Sai Rajeswar, Perouz Taslakian
| Challenge: | Despite being widely used, building workflows can be complex, often requiring manual configuration through low-code platforms or visual programming tools. |
| Approach: | They propose a framework for generating structured workflow outputs from sketches using vision-language models to automate the process. |
| Outcome: | The proposed framework outperforms large vision-language models in the task of generating structured workflow outputs from sketches and diagrams. |
RelationalCoder: Rethinking Complex Tables via Programmatic Relational Transformation (2025.acl-long)
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| Challenge: | Semi-structured tables remain a major obstacle for automated data processing and analytics. |
| Approach: | They propose a technique called Loop Reference Decoding which identifies expandable groups and replicates each group using a concise loop over its repetitive region. |
| Outcome: | The proposed technique reduces output length from O(N M) to approximately O(K) Extensive experiments on HiTab and MultiHiertt show that it boosts Llama-2 and Mistral models by more than 20%, and GPT-4o by over 4%. |
Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models (2025.findings-emnlp)
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Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen, Qinghang Bao, Sheng Wang, Pengan Chen, Jiuming Wang, Lingpeng Kong, Yu Li, Chuan Wu
| Challenge: | Cantonese is considered a low-resource language due to the dominance of Mandarin . rich colloquial vocabulary of Cantone, English loanwords, and code-switching characteristics add to the complexity of corpus collection and processing. |
| Approach: | We collect Cantonese texts from open source corpora, Hong Kong-specific forums, Wikipedia . we refine the model through supervised fine-tuning on curated Cantonesian tasks . |
| Outcome: | The model achieves state-of-the-art (SOTA) performance on four Cantonese benchmarks. |
LeConTra: A Learner Corpus of English-to-Dutch News Translation (2022.lrec-1)
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| Challenge: | a dataset of English-to-Dutch news translations enriched with translation process data is available for free . three students of a Master's programme in Translation were asked to translate 50 different English journalistic texts of approximately 250 tokens each. |
| Approach: | They propose to make a learner corpus of English-to-Dutch news translations enriched with translation process data. |
| Outcome: | The dataset can be used in translation process research, learner corpus research, and corpus-based translation studies. |
TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning (2025.findings-naacl)
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| Challenge: | Current Large Language Models lack ability to understand table structures and apply precise numerical reasoning. |
| Approach: | They propose a tool-augmented reasoning framework for table-based tasks that integrates LLMs with specialized tools. |
| Outcome: | The proposed framework improves on the TOOLTAB dataset, a benchmark for LLMs in table–tool integration. |
Butterfly Effects in Frame Semantic Parsing: impact of data processing on model ranking (C18-1)
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| Challenge: | a common contribution to computational linguistics research is a new model for a specific task. |
| Approach: | They propose an open-source standardized processing pipeline for frame semantic parsing . they propose a standard evaluation resource that can be shared and reused for robust comparison . |
| Outcome: | The proposed model can be shared and reused for robust model comparison. |
Exploring Representation-level Augmentation for Code Search (2022.emnlp-main)
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| Challenge: | Recent data augmentations for code search are at the raw-data level, which requires additional code analysis and training cost. |
| Approach: | They propose a general format of representation-level augmentation that unifies existing methods. |
| Outcome: | The proposed methods can boost the performance of code search models on a large-scale dataset. |
CodeV: Issue Resolving with Visual Data (2025.findings-acl)
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Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Huang Pengjie, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang
| Challenge: | Large Language Models (LLMs) have expanded to more complex repository-level tasks. |
| Approach: | They propose a first approach to leveraging visual data to enhance the issue-resolving capabilities of Large Language Models (LLMs) they demonstrate the effectiveness of CodeV and provide valuable insights into leveraging visualization to resolve GitHub issues. |
| Outcome: | The proposed approach improves the issue-resolving capabilities of Large Language Models (LLMs) by using visual data. |
Development of Automatic Speech Recognition for the Documentation of Cook Islands Māori (2022.lrec-1)
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Rolando Coto-Solano, Sally Akevai Nicholas, Samiha Datta, Victoria Quint, Piripi Wills, Emma Ngakuravaru Powell, Liam Koka’ua, Syed Tanveer, Isaac Feldman
| Challenge: | a new study describes the process of data processing and training of an automatic speech recognition system for Cook Islands Mori . the system is based on statistical and Deep Learning techniques, and is available under a license . |
| Approach: | They describe the process of data processing and training of an automatic speech recognition system for Cook Islands Mori . they transcribed four hours of speech from adults and elderly speakers of the language and prepared two experiments . |
| Outcome: | The proposed system can perform better with low-resource Indigenous languages . the system can be used to accelerate the documentation of Cook Islands Mori . |
End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation (2023.emnlp-main)
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Juan Pablo Zuluaga-Gomez, Zhaocheng Huang, Xing Niu, Rohit Paturi, Sundararajan Srinivasan, Prashant Mathur, Brian Thompson, Marcello Federico
| Challenge: | Conventional speech-to-text translation systems are trained on single-speaker utterances, but they may not be applicable to real-life scenarios where the audio contains conversations by multiple speakers. |
| Approach: | They propose a speaker-turn-aware conversational speech translation model that integrates automatic speech recognition, speech translation and speaker turn detection using special tokens in a serialized labeling format. |
| Outcome: | The proposed model outperforms the reference systems on the multi-speaker condition while attaining comparable performance on the single-speakspeaker conditions. |
CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data (2020.lrec-1)
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Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, Edouard Grave
| Challenge: | Pre-training text representations have led to significant improvements in many areas of natural language processing. |
| Approach: | They propose a pipeline to extract monolingual datasets from Common Crawl . pipeline follows data processing introduced in fastText that deduplicates documents . |
| Outcome: | The proposed pipeline performs standard document deduplication and language identification similar to the pipeline introduced in fastText and a filtering step to select documents close to high quality corpora like Wikipedia. |
E:Calm Resource: a Resource for Studying Texts Produced by French Pupils and Students (2020.lrec-1)
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| Challenge: | É:CALM resource is constructed from handwritten texts produced in a variety of usual contexts of teaching. |
| Approach: | They describe the whole data processing established for digitizing and standardizing such manuscripts in order to provide an ecological resource for NLP, corpus linguistics and POS. |
| Outcome: | The E:CALM resource is constructed from hand-written texts produced in a variety of usual contexts of teaching. |
Confront Insider Threat: Precise Anomaly Detection in Behavior Logs Based on LLM Fine-Tuning (2025.coling-main)
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| Challenge: | Current methods for insider threat detection suffer from low precision and information loss . a novel approach to detect insider threats is needed to improve accuracy . |
| Approach: | They propose a precise anomaly detection solution based on Large Language Model (LLM) fine-tuning . they represent user behavior in natural language and implement a threat tracing mechanism . |
| Outcome: | The proposed solution achieves an F1 score of 0.8941 on the CERT v6.2 dataset . |
Can LLMs Understand the Impact of Trauma? Costs and Benefits of LLMs Coding the Interviews of Firearm Violence Survivors (2026.findings-acl)
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| Challenge: | Firearm violence research remains underfunded and difficult to scale due to the lack of funding from the NIH and CDC. |
| Approach: | They use open-source large language models to inductively code interviews with 21 Black men who have survived community firearm violence. |
| Outcome: | The use of open-source LLMs to inductively code interviews with 21 Black men shows that the models can identify important codes, but that they are highly sensitive to data processing. |
DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment (2025.findings-emnlp)
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| Challenge: | a new framework for training data processing for Chinese medical language models is proposed . experimental results show that the framework significantly improves model accuracy . |
| Approach: | They propose a data processing framework for Chinese medical language models training and deployment . the framework is based on a question-oriented model training strategy and privacy preservation . |
| Outcome: | The proposed framework significantly improves model accuracy and reduces privacy leakage by 27%. |
GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages (2022.lrec-1)
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| Challenge: | Low-resourced languages with similar typologies are often confused with each other in real-world applications such as machine translation, affecting the user’s experience. |
| Approach: | They propose to build a dataset for five typologically and phylogenetically related low-resourced East African languages using the Ge’ez script as a writing system. |
| Outcome: | The proposed dataset is built automatically from selected data sources, but also performed a manual evaluation to assess its quality. |
DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models (2024.emnlp-main)
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Yiming Huang, Jianwen Luo, Yan Yu, Yitong Zhang, Fangyu Lei, Yifan Wei, Shizhu He, Lifu Huang, Xiao Liu, Jun Zhao, Kang Liu
| Challenge: | DA-Code is a code generation benchmark designed to assess LLMs on agent-based data science tasks. |
| Approach: | They propose a code generation benchmark specifically designed for LLMs on agent-based data science tasks. |
| Outcome: | The benchmark performs better than existing frameworks, but lacks accuracy . it is based on real-world data, and includes examples that cover a wide range of tasks . |
Using Automatic Speech Recognition in Spoken Corpus Curation (2020.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) is a new way to make audio-visual data accessible. |
| Approach: | They propose to use automatic speech recognition (ASR) to make audio-visual data accessible by systematic queries. |
| Outcome: | The proposed system has higher recognition scores for the north of Germany vs. lower scores for south of the country. |
IAEval: A Comprehensive Evaluation of Instance Attribution on Natural Language Understanding (2023.findings-emnlp)
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| Challenge: | Instance attribution (IA) aims to identify the training instances leading to the prediction of a test example. |
| Approach: | They propose a systematic and comprehensive evaluation scheme covering four significant requirements: sufficiency, completeness, stability and plausibility. |
| Outcome: | The proposed evaluation scheme covers four significant requirements: sufficiency, completeness, stability and plausibility. |
Intelligent Document Parsing: Towards End-to-end Document Parsing via Decoupled Content Parsing and Layout Grounding (2025.findings-emnlp)
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| Challenge: | Existing methods fragment document parsing into pipeline of separated subtasks, resulting in incomplete semantics and error propagation. |
| Approach: | They propose an end-to-end document parsing framework that leverages vision-language priors of MLLMs. |
| Outcome: | The proposed method surpasses existing methods significantly in document parsing . it leverages the vision-language priors of MLLMs to decouple parse and layout grounding based on visual information. |
Evaluation Pitfalls and Challenges in Multimedia Event Extraction (2026.acl-long)
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| Challenge: | Recent work has focused on textual content, but recent work has explored the integration of additional modalities to support more accurate and comprehensive event understanding. |
| Approach: | They propose to analyze the evaluation pitfalls of multimedia event extraction by combining textual and visual inputs to identify events and their arguments across multiple modalities. |
| Outcome: | The proposed model overestimates performance and performance of the proposed model in a series of controlled experiments under a strict evaluation framework. |