Papers with visualization
OctoTools: A Multi-Agent Framework with Extensible Tools for Complex Reasoning (2026.acl-long)
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| Challenge: | Existing prompting methods for large language models (LLMs) are restricted to specialized domains, limited tool types, or require additional training data. |
| Approach: | They propose a training-free, user-friendly, and easily extensible multi-agent framework designed to tackle complex reasoning across diverse domains. |
| Outcome: | The proposed framework outperforms AutoGen, GPT-Functions, and LangChain by up to 10.6% when given the same set of tools. |
NLP+Vis: NLP Meets Visualization (2023.emnlp-tutorial)
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| Challenge: | This tutorial will introduce NLP+Vis with a focus on two main threads of work: NLP for Vis and Vis for NLP. |
| Approach: | tutorial will introduce NLP+Vis with a focus on two main threads of work . overview of research topics on combining NLP and Vis techniques will be covered . |
| Outcome: | The tutorial will introduce NLP+Vis with a focus on two main threads of work . it will provide an overview of research topics on combining NLP and Vis techniques . |
MapQaTor: An Extensible Framework for Efficient Annotation of Map-Based QA Datasets (2025.acl-demo)
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| Challenge: | Mapping and navigation services struggle to handle natural language geospatial queries. |
| Approach: | They introduce an extensible open-source framework that streamlines the creation of reproducible, traceable map-based QA datasets. |
| Outcome: | a new open-source framework streamlines the creation of reproducible, traceable map-based QA datasets. |
EVIDENCEMINER: Textual Evidence Discovery for Life Sciences (2020.acl-demos)
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Xuan Wang, Yingjun Guan, Weili Liu, Aabhas Chauhan, Enyi Jiang, Qi Li, David Liem, Dibakar Sigdel, John Caufield, Peipei Ping, Jiawei Han
| Challenge: | EVIDENCEMINER is a web-based system that allows users to query a natural language statement and retrieve textual evidence from a background corpora for life sciences. |
| Approach: | They propose a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. |
| Outcome: | EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. |
Exploration and Discovery of the COVID-19 Literature through Semantic Visualization (2021.naacl-srw)
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| Challenge: | Existing semantic visualization methods are limited in finding connections between corpora targeting a specific topic. |
| Approach: | They propose to use semantic visualization to explore large datasets of complex networks by exploiting the semantics of the relations in them. |
| Outcome: | The proposed method can enable exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them. |
KeywordScape: Visual Document Exploration using Contextualized Keyword Embeddings (2022.emnlp-demos)
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| Challenge: | Existing tools for document visualization assume that keywords have static meanings, but contextualized word embeddings are unrealistic. |
| Approach: | They propose a visual exploration tool that visualizes contextualized word embeddings in documents based on keywords. |
| Outcome: | The proposed tool visualizes keywords in terms of their contextualized embeddings in a semantic landscape that keeps keywords with similar context close to each other, allowing for a more precise search and comparison of documents. |
Humanoid Agents: Platform for Simulating Human-like Generative Agents (2023.emnlp-demo)
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| Challenge: | Humanoid Agents aims to guide Generative Agents to behave more like humans using System 1 processing . we introduce three elements of System 1 that can influence their behavior . |
| Approach: | They propose a system that guides Generative Agents to behave more like humans . they introduce three elements of System 1 processing that can influence their behavior . humanoid agents can use these dynamic elements to adapt their daily activities and conversations . |
| Outcome: | The proposed system guides Generative Agents to behave more like humans . it incorporates three elements of System 1 processing that can influence their behavior . humanoid agents can adapt their daily activities and conversations with other agents . |
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models (2020.emnlp-demos)
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Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, Ann Yuan
| Challenge: | Existing tools for modeling and understanding models are limited . existing tools can assist practitioners in understanding and evaluating models . |
| Approach: | They present an open-source platform for visualization and understanding of NLP models. |
| Outcome: | The language interpretability tool (lit) is an open-source platform for visualization and understanding of NLP models. |
Improving Personalized Explanation Generation through Visualization (2022.acl-long)
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| Challenge: | Existing explainable recommendation models generate repetitive sentences for different items or empty sentences with insufficient details. |
| Approach: | They propose a visual-enhanced approach to generate rating scores and text explanations using visualization generation and text–image matching discrimination. |
| Outcome: | The proposed approach improves both the text quality and the diversity and explainability of the generated explanations. |
A Data-Centric Framework for Composable NLP Workflows (2020.emnlp-demos)
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Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, Zhiting Hu
| Challenge: | Empirical natural language processing (NLP) systems involve interoperation among multiple components . a wealth of NLP toolkits exist ( 4), such as spaCy, DKPro, CoreNLP. |
| Approach: | They propose a unified open-source framework that supports fast development of NLP workflows . framework includes processors for NLP tasks, visualization, and annotation . |
| Outcome: | The framework offers processors for NLP tasks, visualization, and annotation, and is extensible . it is delivered through two modularized yet integratable open-source projects, Forte and Stave . |
The Why and The How: A Survey on Natural Language Interaction in Visualization (2022.naacl-main)
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| Challenge: | Recent research shows that different forms of natural language-based interaction prove suitable to support users in accomplishing various visualization tasks. |
| Approach: | They propose a taxonomy of visualization tasks and a classification system to illustrate the state-of-the-art of natural language-based interaction in visualization. |
| Outcome: | The proposed model can support annotations, recommendations, explanations, and documentation tasks. |
LOA: Logical Optimal Actions for Text-based Interaction Games (2021.acl-demo)
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Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander Gray
| Challenge: | et al., 2019) have proposed a neuro-symbolic approach for reinforcement learning in non-simultaneous environments. |
| Approach: | They propose an action decision architecture with a neuro-symbolic framework for natural language interaction games. |
| Outcome: | The proposed framework provides an open-source implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. |
MedDecXtract: A Clinician-Support System for Extracting, Visualizing, and Annotating Medical Decisions in Clinical Narratives (2025.acl-demo)
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| Challenge: | Clinical notes contain important information about medical decisions embedded within unstructured text. |
| Approach: | They propose an open-source interactive system that automatically extracts medical decisions from clinical text. |
| Outcome: | The open-source system extracts and visualizes medical decisions from clinical text. |
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. |
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding (2022.findings-naacl)
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| Challenge: | Existing methods for encoding text in tables require additional training and require additional pretraining. |
| Approach: | They propose a novel encoding strategy that preserves the critical property of permutation invariance across rows or columns. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three table interpretation tasks: column type annotation, relation extraction, and entity linking. |
The ACoLi CoNLL Libraries: Beyond Tab-Separated Values (L18-1)
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| Challenge: | a new set of Java archives facilitates advanced manipulations of corpora annotated in TSV formats. |
| Approach: | They propose to use Java archives to facilitate advanced manipulations of corpora annotated in TSV formats. |
| Outcome: | The proposed libraries support all members of the CoNLL format family. |
Fin-ExBERT: User Intent based Text Extraction in Financial Context using Graph-Augmented BERT and trainable Plugin (2025.emnlp-industry)
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| Challenge: | Financial dialogue transcripts pose a unique challenge for sentence-level information extraction due to their informal structure, domain-specific vocabulary, and variable intent density. |
| Approach: | They propose a framework for extracting user intent–relevant sentences from financial service calls. |
| Outcome: | The proposed framework shows strong precision and F1 performance on real-world transcripts . financial transcripts are a challenge due to their informal structure and domain-specific vocabulary . |
TensorLens: End-to-End Transformer Analysis via High-Order Attention Tensors (2026.acl-long)
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| Challenge: | Existing attention-aggregation methods focus on individual attention heads or layers, failing to account for the model’s global behavior. |
| Approach: | They propose a unified attention representation that captures the entire transformer as a single, input-dependent linear operator expressed through a high-order attention-interaction tensor. |
| Outcome: | The proposed model encapsulates the entire transformer as a single, input-dependent linear operator expressed through a high-order attention-interaction tensor. |
PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context (2021.acl-long)
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| Challenge: | Creating effective visualizations is a challenge given the myriad of parameters that users need to provide. |
| Approach: | They propose to synthesize visualization programs from natural language utterances and programmatic context using PlotCoder. |
| Outcome: | The proposed architecture models both the code context and the input utterance. |
Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models (2023.eacl-main)
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Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer
| Challenge: | Pretrained language models store a large amount of factual information that can be elicited by prompting or finetuning. |
| Approach: | They propose methods to measure model factual beliefs and update incorrect beliefs in models . they propose a new visualization tool that shows relationships between stored model beliefs . |
| Outcome: | The proposed methods improve models' consistency and accuracy, the authors show . their methods outperform existing methods in more difficult settings, the paper shows . |
Personalized Neural Embeddings for Collaborative Filtering with Text (N19-1)
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| Challenge: | Traditional CF approaches exploit user-item relations only and suffer from data sparsity issues. |
| Approach: | They develop a Personalized Neural Embedding framework to exploit both interactions and words seamlessly. |
| Outcome: | The proposed framework exploits both interactions and words seamlessly and predicts user preferences on items based on these embeddings. |
TS-CLIP: Time Series Understanding by CLIP (2025.emnlp-main)
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| Challenge: | Contrastive Language–Image Pre-training (CLIP) has demonstrated remarkable success in aligning vision and language. |
| Approach: | They propose a synonym bank mechanism that generates synonym embeddings as alignment targets. |
| Outcome: | The proposed approach achieves state-of-the-art (SOTA) performance on 51 datasets. |
ZAP: An Open-Source Multilingual Annotation Projection Framework (L18-1)
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| Challenge: | Existing frameworks for annotation projection in parallel corpora limit reproducibility and comparison of experiments. |
| Approach: | They propose an open-source framework for annotation projection in parallel corpora . framework is Java-based and includes methods for preprocessing corpors, computations and visualization . |
| Outcome: | The proposed framework is designed for ease-of-use with lightweight APIs. |
Development of a Mobile Observation Support System for Students: FishWatchr Mini (L18-1)
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| Challenge: | Several video annotation tools have been developed to observe educational activities, but they are not suitable for students' real-time annotation and group reflection. |
| Approach: | They propose a system called FishWatchr Mini which supports students' observation and reflection in the classroom. |
| Outcome: | The proposed system allows students to examine annotation data through reflection, by providing functions such as visualization. |
Protecting multimodal large language models against misleading visualizations (2026.acl-long)
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| Challenge: | MLLMs are robust to misleading visualizations, i.e., charts that distort the underlying data, leading readers to draw inaccurate conclusions. |
| Approach: | They propose to use table-based QA and redrawing the visualization to improve QA performance on misleading visualizations. |
| Outcome: | The proposed methods improve MLLM question-answering accuracy on misleading visualizations without compromising accuracy on non-misleading ones. |
MarkupLM: Pre-training of Text and Markup Language for Visually Rich Document Understanding (2022.acl-long)
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| Challenge: | Existing layout-based pre-training approaches are not easy to apply to VRDU tasks. |
| Approach: | They propose to use markup languages as the backbone for document understanding tasks where text and markup information are jointly pre-trained. |
| Outcome: | The proposed model outperforms existing models on document understanding tasks. |
When Annotators Disagree, Topology Explains: Mapper, a Topological Tool for Exploring Text Embedding Geometry and Ambiguity (2025.emnlp-main)
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| Challenge: | Language models are evaluated with scalar metrics like accuracy but do not capture how they encode ambiguity and more generally instances. |
| Approach: | They propose to analyze how fine-tuned models encode ambiguity and more generally instances. |
| Outcome: | The proposed tool uncovers decision regions, boundary collapses, and overconfident clusters in a RoBERTa-Large dataset. |
Auto-Encoding Variational Bayes for Inferring Topics and Visualization (2020.coling-main)
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| Challenge: | Visualization and topic modeling are widely used for text analysis, but there is a lack of semantic interpretation as they do not extract topics. |
| Approach: | They propose a fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization using a single generative model. |
| Outcome: | The proposed method can handle model changes efficiently with little rederivation effort. |
Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows (2026.findings-acl)
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Haoyu Dong, Pengkun Zhang, Yan Gao, Xuanyu Dong, Yilin Cheng, Mingzhe Lu, Adina Yakefu, Shuxin Zheng
| Challenge: | FinWorkBench evaluates real-world enterprise-grade finance and accounting workflows . a human evaluation of GPT 5.1 Pro passes only 38.4% of workflows, a study finds . |
| Approach: | They propose a workflow construction process that combines LLM-assisted mining and expert annotation to build 172 composite workflows. |
| Outcome: | The proposed process combines expert annotation with LLM-assisted mining of workflows from authentic enterprise environments. |
Linking, Searching, and Visualizing Entities in Wikipedia (L18-1)
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| Challenge: | Existing systems to extract, index, search, and visualize entities in Wikipedia are not strings, but unique identifiers from Wikidata. |
| Approach: | They propose a system to extract, index, search, and visualize entities in Wikipedia . they use a document model to store linguistic annotations and a string matching engine . |
| Outcome: | The proposed system achieves CEAFm scores of 70.0 on English, 64.4 on Chinese, and 66.5 on Spanish. |
Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery (D19-1)
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| Challenge: | Existing approaches to inference opinionated text do not capture attributes in a one-off manner. |
| Approach: | They propose a probabilistic model that discovers aspects and sentiments from text and associates them with different attributes. |
| Outcome: | The proposed model outperforms state-of-the-art models and yields intuitive topics. |
Visualizing the “Dictionary of Regionalisms of France” (DRF) (L18-1)
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| Challenge: | a corpus of regionalisms, parts of speech and recognition rates is published in the Dictionnaire des Régionalismes de France. |
| Approach: | They propose to curate and analyze the corpus of regionalisms published in the Dictionnaire des Régionalismes de France. |
| Outcome: | The corpus contains all entries in the DRF for which recognition rates were recorded . the analysis compares with previous work on regionalalisms and atlas . |
FoTo: Targeted Visual Topic Modeling for Focused Analysis of Short Texts (2024.lrec-main)
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| Challenge: | Existing topic models perform a full corpus analysis that treats all topics equally, making them not on target. |
| Approach: | They propose a targeted topic model for semantic short-text embedding which aims to learn all topics and low-dimensional visual representations of documents while preserving relevant topics. |
| Outcome: | The proposed model learns all topics and low-dimensional visual representations while preserving relevant topics in the visualization space. |
Chart2Code53: A Large-Scale Diverse and Complex Dataset for Enhancing Chart-to-Code Generation (2025.emnlp-main)
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Tianhao Niu, Yiming Cui, Baoxin Wang, Xiao Xu, Xin Yao, Qingfu Zhu, Dayong Wu, Shijin Wang, Wanxiang Che
| Challenge: | Existing Chart2code-related training datasets suffer from limited scale, limited type coverage, and inadequate complexity. |
| Approach: | They propose to synthesize chart2code-related training datasets using web plotting code and chart images to address these challenges. |
| Outcome: | The proposed dataset exhibits the greatest diversity and higher complexity compared to other open-source Chart2code related datasets. |
“Seeing the Big through the Small”: Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations? (2024.findings-emnlp)
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| Challenge: | Human label variation arises when multiple human annotators provide different labels for valid reasons. |
| Approach: | They propose to use crowd workers to represent human judgment distributions or expert linguists to provide detailed explanations for their chosen labels. |
| Outcome: | The proposed model can approximate human judgment distributions using a small number of expert labels and explanations. |
emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation (2024.findings-acl)
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| Challenge: | Existing models for speech emotion recognition are not suitable for emotional tasks. |
| Approach: | They propose a universal speech emotion representation model that is pre-trained on open-source emotion data. |
| Outcome: | euphoria2vec outperforms state-of-the-art models and emotion specialist models . it shows consistent improvements among 10 different languages of speech emotion recognition datasets . |