Papers with data analysis
MaintNet: A Collaborative Open-Source Library for Predictive Maintenance Language Resources (2020.coling-demos)
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| Challenge: | Maintenance record logbooks are an emerging text type in NLP. maintenance record logbook data is often written in non-standard language with many domain specific technical terms, abbreviations, and non-standardized spelling and grammar. |
| Approach: | They propose to create a collaborative open-source library of technical and domain-specific language resources for maintenance record logbooks. |
| Outcome: | The proposed library provides tools to aid in their (pre-)processing and clustering. |
TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation (2022.aacl-demo)
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Lorenz Stangier, Ji-Ung Lee, Yuxi Wang, Marvin Müller, Nicholas Frick, Joachim Metternich, Iryna Gurevych
| Challenge: | TexPrax is a messaging system to collect and annotate task-oriented dialog data . informal communication channels such as instant messengers are increasingly being used at work . |
| Approach: | They propose a messaging system that collects and annotates task-oriented dialog data from employees via chatbots. |
| Outcome: | The proposed system collects and annotates tasks-oriented dialog data from german factory workers and provides lightweight annotations. |
Contrastive Explanations of Text Classifiers as a Service (2022.naacl-demo)
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| Challenge: | ContrXT provides time contrastive explanations of black box text classifiers by manipulating binary decision diagrams. |
| Approach: | They propose a system that provides time contrastive explanations of black box classifiers as a service by manipulating binary decision diagrams. |
| Outcome: | The proposed system has a throughput of 2.55 users per second and is available as a python pip package. |
SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization (2021.acl-demo)
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| Challenge: | despite advances in abstractive text summarization, the true performance and failure modes of modern neural models are not yet fully understood due to the black-box nature of neural models and unmanageable scale of recent datasets for manual analysis. |
| Approach: | They propose an open-source tool for visualizing abstractive summaries that enables fine-grained analysis of models, data, and evaluation metrics associated with text summarization. |
| Outcome: | The proposed tool can identify the shortcomings and failure modes of state-of-the-art summarization models. |
An LLM-Based Approach for Insight Generation in Data Analysis (2025.naacl-long)
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| Challenge: | Existing approaches to generate insightful data from databases are time-consuming and resource-intensive. |
| Approach: | They propose a method that leverages Large Language Models to automatically generate textual insights from databases. |
| Outcome: | The proposed approach generates more insightful insights than other approaches while maintaining correctness. |
PAWLS: PDF Annotation With Labels and Structure (2021.acl-demo)
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| Challenge: | Existing tools for annotation of PDFs are limited to a web browser, allowing users to extract semantically meaningful regions from PDFs. |
| Approach: | They propose an annotation tool specifically designed for Adobe’s Portable Document Format (PDF) PAWLS supports span-based textual annotation, N-ary relations and freeform, non-textual bounding boxes. |
| Outcome: | The proposed tool supports span-based textual annotation, N-ary relations and freeform, non-textual bounding boxes. |
InsightPilot: An LLM-Empowered Automated Data Exploration System (2023.emnlp-demo)
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| Challenge: | InsightPilot is an LLM-based, automated data exploration system designed to simplify the data exploration process. |
| Approach: | They propose an LLM-based, automated data exploration system that streamlines the data exploration process. |
| Outcome: | InsightPilot is an LLM-based, automated data exploration system . it can help users gain valuable insights from their datasets, in a case study and in nl . |
VeriMinder: Mitigating Analytical Vulnerabilities in NL2SQL (2025.acl-demo)
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| Challenge: | Application systems using natural language interfaces to databases (NLIDBs) have democratized data analysis, but they are not without significant risks. |
| Approach: | They propose an interactive system that detects and mitigates cognitive biases in analytical questions by using contextual semantic mapping frameworks. |
| Outcome: | The proposed system detects and mitigates cognitive biases in analytical questions and generates high-quality, task-specific prompts. |
Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review (2025.acl-long)
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| Challenge: | Clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process. |
| Approach: | They propose to use AI to improve evaluation methods and propose new methods to assist clinical coders in their workflows. |
| Outcome: | The proposed methods can be improved and improved on existing methods and the existing ones to assist coders in their workflows. |
ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild (2025.coling-industry)
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| Challenge: | Existing methods for chart understanding and reasoning are weakly aligned and rely on underlying data tables. |
| Approach: | They propose a chart-based understanding and reasoning model that is trained on instruction-tuning data generated directly from chart images. |
| Outcome: | The proposed model achieves state-of-the-art results across 5 benchmarks spanning chart summarization, question answering, and fact-checking. |
STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants (2023.emnlp-industry)
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Leon Zhang, Jiarui Lu, Joel Ruben Antony Moniz, Aditya Kulkarni, Dhivya Piraviperumal, Tien Dung Tran, Nick Tzou, Hong Yu
| Challenge: | Existing training datasets for steering use cases are limited due to the cold-start problem. |
| Approach: | They propose a steering detection model that predicts whether a follow-up turn is a user’s attempt to steer the previous command. |
| Outcome: | The proposed model outperforms existing models on human-graded evaluation sets and shows that it can identify steering intent with over 95% accuracy. |
Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly integrated into everyday workflows . a recent study found that LLMs exhibit distinct personality-like traits that affect user engagement . |
| Approach: | They evaluated 32 LLM users for four collaborative tasks and found significant preferences . they found that rationalists preferred GPT-4, while idealists favored Claude 3.5 . |
| Outcome: | The results show that users with different personality traits prefer certain LLMs over others. |
QTSumm: Query-Focused Summarization over Tabular Data (2023.emnlp-main)
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Yilun Zhao, Zhenting Qi, Linyong Nan, Boyu Mi, Yixin Liu, Weijin Zou, Simeng Han, Ruizhe Chen, Xiangru Tang, Yumo Xu, Dragomir Radev, Arman Cohan
| Challenge: | Existing text generation systems that can provide accurate table summaries can facilitate more efficient access to relevant data insights. |
| Approach: | They propose a query-focused task where text generation models have to perform human-like reasoning and analysis over the given table to generate a tailored table summary. |
| Outcome: | The proposed method improves existing baselines on table-to-text generation and large language models by concatenating generated facts to the model input. |
Tackling the Story Ending Biases in The Story Cloze Test (P18-2)
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| Challenge: | Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning. |
| Approach: | They propose to use a crowdsourcing scheme to create a new SCT dataset to overcome some of the biases discovered in the original SCT. |
| Outcome: | The proposed model performs better than the baselines on the SCT dataset, despite human-authorship biases. |
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization (2021.findings-emnlp)
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| Challenge: | Current news summarization systems often contain 'extrinsic hallucinations', i.e. facts that are not present in the source document, which are often derived via world knowledge. |
| Approach: | They propose to use multiple supplementary resource documents to assist the task by pairing a single document with a human authored summary as the summary. |
| Outcome: | The proposed model reduces 55% of hallucinations when compared to single-document summarization models trained on the main article only. |
Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types (C18-1)
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| Challenge: | Annotated corpora are often assigned to internet workers whose judgments are reconciled by crowdsourcing models. |
| Approach: | They propose a framework for learning from rich prior knowledge to combine annotations with different structures. |
| Outcome: | The proposed model compares favorably with previous work and enables active sample selection to reduce annotation effort. |
NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries (2024.findings-eacl)
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Wei Zhao, Zhitao Hou, Siyuan Wu, Yan Gao, Haoyu Dong, Yao Wan, Hongyu Zhang, Yulei Sui, Haidong Zhang
| Challenge: | Creating spreadsheet formulas remains a tedious and error-prone task for many end-users . a novel task is proposed to generate spreadsheet formulae from a user's NL query . |
| Approach: | They propose a task to generate formulas that are grounded on a spreadsheet table given a Natural Language query as input. |
| Outcome: | The proposed task generates formulas that are grounded on a spreadsheet table, given a natural language query as input. |
ICLER: Intent CLassification with Enhanced Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for intent classification are inadequate in identifying micro-grained intentions . ICLER is based on In-Context Learning, but it is inadequate in enterprise vertical domains . |
| Approach: | They propose an intent classification method with enhanced reasoning that optimizes the embedding model to capture subtle sentence-level information. |
| Outcome: | The proposed method outperforms existing methods in intent identification tasks in vertical domains. |
Nonparametric Forest-Structured Neural Topic Modeling (2022.coling-1)
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| Challenge: | Existing hierarchical neural topic models can only extract topics at the same level. |
| Approach: | They propose to use self-attention mechanism to capture parent-child topic relationships and build a sparse directed acyclic graph to form a topic forest. |
| Outcome: | The proposed model outperforms baseline models on topic hierarchical rationality and affinity. |
Benchmarking Data Science Agents (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have emerged as promising data science aids, assisting humans in data analysis and processing. |
| Approach: | They propose an evaluation paradigm and benchmarks that assess the performance of data science agents throughout the entire data science lifecycle. |
| Outcome: | The proposed evaluation paradigm streamlines dataset preparation, improves coverage, and expands benchmarking comprehensiveness. |
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings (2022.coling-1)
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Jason Ingyu Choi, Saar Kuzi, Nikhita Vedula, Jie Zhao, Giuseppe Castellucci, Marcus Collins, Shervin Malmasi, Oleg Rokhlenko, Eugene Agichtein
| Challenge: | Existing Conversational Task Assistants fail to provide a comprehensive natural conversation that includes search, context-aware QA, step-by-step instructions. |
| Approach: | They present a corpus of conversations in two domains: cooking and home improvement . they crowd-sourced 549 conversations with an asynchronous Wizard-of-Oz setup . |
| Outcome: | The proposed model performs well in both Intent Classification and Abstractive Question Answering tasks, but the performance is poor on AQA tasks. |
PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers (2024.naacl-long)
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| Challenge: | Existing methods for decision making require complex data analysis. |
| Approach: | They propose a method that generates the plan for decision making as the first step and retrieves the queries for data analysis as the second step. |
| Outcome: | The proposed method outperforms the state-of-the-art iterative plan-then-retrieval augmented generation method by 15.8% and 7.4% respectively. |
Dynamic Stance: Modeling Discussions by Labeling the Interactions (2023.findings-emnlp)
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| Challenge: | Stance detection is a popular task that has been modeled as a static task, but its limitations are strong topic-dependent. |
| Approach: | They propose to model stance as a dynamic task by focusing on interactions between a message and their replies. |
| Outcome: | The proposed model shows portability across topics and languages. |
A New Dataset and Empirical Study for Sentence Simplification in Chinese (2023.acl-long)
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| Challenge: | Sentence simplification is a valuable technique that can benefit language learners and children. |
| Approach: | They propose a dataset for assessing sentence simplification in Chinese using manual simplifications from human annotators. |
| Outcome: | The proposed dataset shows that Chinese sentences are more accessible to children and nonnative readers than English sentences. |
MAPWise: Evaluating Vision-Language Models for Advanced Map Queries (2025.naacl-long)
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| Challenge: | Vision-language models excel at tasks requiring joint understanding of visual information and natural language. |
| Approach: | They propose to use choropleth maps to answer questions from three geographical regions in the United States, India, China as question templates. |
| Outcome: | The proposed model outperforms other models in the area of visual language and visual question answering. |
AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science (2025.findings-emnlp)
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| Challenge: | Large language models are increasingly used to automate data analysis, but data science tasks often admit multiple statistically valid solutions. |
| Approach: | They propose a framework to evaluate LLM-generated code and assess its reproducibility . they introduce two reproducibility-enhancing prompting strategies and benchmark them against standard prompting . |
| Outcome: | The proposed framework improves reproducibility of large language models . it provides a foundation for transparent, reliable, and efficient human–AI collaboration in data science. |
Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data (2024.findings-acl)
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| Challenge: | Quantitative reasoning with data is a critical skill to analyze data, yet the assessment of such ability remains limited. |
| Approach: | They propose a quantitative reasoning with data benchmark to evaluate Large Language Models' ability in statistical and causal reasoning with real-world data. |
| Outcome: | The proposed model GPT-4 achieves an accuracy of 58%, while open-source model Deepseek-coder-instruct gets the highest accuracy of 37%. |
MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup (2023.emnlp-main)
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| Challenge: | Disfluency detection models focus on individual utterances, but discontinuities in spoken transcripts occur across multiple turns. |
| Approach: | They propose a multi-turn "cleanup task" to detect discontinuities in spoken conversations . they leverage two modeling approaches for experimental evaluation as benchmarks . |
| Outcome: | The proposed task detects "discontinuities" in spoken conversations that can be removed . the results are compared with existing methods and are expected to be validated in the future . |
The Lexometer: A Shiny Application for Exploratory Analysis and Visualization of Corpus Data (2022.lrec-1)
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| Challenge: | Lexometer is a data science application that integrates data analysis and visualization functions into an easy-to-use graphical user interface. |
| Approach: | They propose a Shiny application that integrates data analysis and visualization functions into an easy-to-use graphical user interface. |
| Outcome: | The Lexometer integrates numerous data analysis and visualization functions into an easy-to-use graphical user interface. |
Automatic Analysis of Substantiation in Scientific Peer Reviews (2023.findings-emnlp)
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| Challenge: | Existing systems to analyze peer reviews' quality are inadequate due to the increasing workload of reviewers and the lack of domain experts . |
| Approach: | They propose to use a claim-evidence pair extraction problem to analyze substantiation in peer reviews and train an argument mining system to do the same. |
| Outcome: | The proposed system could be used by conference managers and reviewers to analyze the quality of peer reviews. |
CluHTM - Semantic Hierarchical Topic Modeling based on CluWords (2020.acl-main)
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| Challenge: | Hierarchical Topic modeling (HTM) exploits latent topics and relationships among them as a powerful tool for data analysis and exploration. |
| Approach: | They propose a hierarchical matrix factorization that exploits latent topics and relationships among them to create a powerful tool for data analysis and exploration. |
| Outcome: | The proposed method outperforms baselines and datasets in the vast majority of cases. |
DataSeer: A Manager-Centric Collaborative Multi-Agent Framework with Multi-Branch Reasoning for Automated Insight Discovery (2026.findings-acl)
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| Challenge: | Existing methods for automated insight discovery lack contextual coherence and coverage due to single-path exploration. |
| Approach: | They propose a Manager-Centric Collaborative Framework that integrates planner and executor . it ensures cross-episode contextual coherence and allows for adaptive sub-goal generation . |
| Outcome: | The proposed framework outperforms baselines on InsightBench and Inseval. |
UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning (2023.emnlp-main)
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| Challenge: | Existing methods for chart-based data analysis neglect explicit modeling of chart structures. |
| Approach: | They propose a pretrained model for chart comprehension and reasoning that encodes relevant text, data, and visual elements of charts and uses a chart-grounded text decoder for text generation. |
| Outcome: | The proposed model outperforms existing methods that lack explicit modeling of chart structures and lacks explicit modeling. |
Sign2Vis: Automated Data Visualization from Sign Language (2025.findings-acl)
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| Challenge: | Existing methods to translate natural language descriptions into visualization queries focus on spoken languages, not sign languages. |
| Approach: | They propose a sign language interface that enables the DHH community to engage more fully with data analysis. |
| Outcome: | The proposed interface can be used by the deaf and hard-of-hearing community. |
DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal (2025.acl-long)
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| Challenge: | Existing approaches to developing LLM-powered coding agents struggle with sub-optimal decision-making. |
| Approach: | They propose a novel inference time compute scaling approach that recovers from sub-optimal decisions by branching out a trajectory at certain key decision points by taking an alternative action given the history of the trajectory and execution feedback of the previous attempt. |
| Outcome: | The proposed approach achieves a pass@1 rate of 47% on the SWE-Bench Lite benchmark, outperforming state-of-the-art (SOTA) open-source frameworks. |
MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection (2024.lrec-main)
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| Challenge: | a new dataset of misinformation labels is being developed to detect misinformation on social media platforms . misinformation is spread in many domains including but not limited to health, politics, and disasters . |
| Approach: | They construct a dataset of 5,284 English and 5,064 Turkish tweets with misinformation labels . they use the dataset to analyze misinformation spread and to evaluate misinformation detection . |
| Outcome: | The proposed dataset includes 5,284 English and 5,064 Turkish tweets with misinformation labels for several recent events between 2020 and 2022. |
Motion Capture Analysis of Verb and Adjective Types in Austrian Sign Language (ÖGS) (2024.lrec-main)
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Julia Krebs, Evguenia A. Malaia, Isabella Fessl, Hans-Peter Wiesinger, Dietmar Roehm, Ronnie Wilbur, Hermann Schwameder
| Challenge: | Temporal and spatial characteristics of dominant hand articulation are used to express semantic and grammatical features across sign languages. |
| Approach: | They use motion capture data from four Deaf signers to characterize kinematic parameters of sign production in verbs and adjectives. |
| Outcome: | The results show that the endpoint marking in verbs and marking of intensification in adjectives are expressed by movement modulation in GS. |
Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback (2025.acl-long)
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Jiakang Yuan, Xiangchao Yan, Bo Zhang, Tao Chen, Botian Shi, Wanli Ouyang, Yu Qiao, Lei Bai, Bowen Zhou
| Challenge: | Recent studies show that AI-assisted research methods can improve research efficiency . a closed-loop framework is used to enhance the automation level of scientific research . |
| Approach: | They propose a closed-loop LLM-driven framework to enhance the automation level of scientific research. |
| Outcome: | The proposed framework improves the efficiency of scientific research by improving data analysis, accelerating computation, and fostering novel idea generation. |
Exploring Intra and Inter-language Consistency in Embeddings with ICA (2024.emnlp-main)
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| Challenge: | Existing studies have shown that ICA can reveal universal semantic axes across languages but lack verification of consistency of independent components within and across languages. |
| Approach: | They propose to use independent component analysis to identify independent components that are more interpretable than PCA to find universal semantic axes. |
| Outcome: | The proposed framework ensures the reliability and universality of semantic axes. |