Papers with HTML

32 papers
Struc-Bench: Are Large Language Models Good at Generating Complex Structured Tabular Data? (2024.naacl-short)

Copied to clipboard

Challenge: Large Language Models (LLMs) have advanced capabilities but produce complex structured data.
Approach: They propose a structure-aware fine-tuning method to bolster LLMs' performance by crafting format-specific instructions from the intended outputs.
Outcome: The proposed method outperforms LLMs on all three formats and spans text tables, HTML, and LaTeX formats.
Vis-Eval Metric Viewer: A Visualisation Tool for Inspecting and Evaluating Metric Scores of Machine Translation Output (N18-5)

Copied to clipboard

Challenge: Many metrics have been proposed for Machine Translation (MT) that compare system translations against human references.
Approach: They propose to use BLEU and METEOR to evaluate machine translations against human translations.
Outcome: VisEval Metric Viewer (VEMV) provides visualisation of multiple evaluation scores so they can be easily interpreted by a user.
GATE Teamware 2: An open-source tool for collaborative document classification annotation (2023.eacl-demo)

Copied to clipboard

Challenge: GATE Teamware 2 is an open-source web-based platform for managing teams of annotators working on document classification tasks.
Approach: They present GATE Teamware 2: an open-source web-based platform for managing teams of annotators working on document classification tasks.
Outcome: GATE Teamware 2 is an open-source web-based platform for managing teams of annotators working on document classification tasks.
Improving Knowledge Base Construction from Robust Infobox Extraction (N19-2)

Copied to clipboard

Challenge: Existing knowledge bases are incomplete, resulting in poor answers and incompleteness.
Approach: They propose a method to extract Wikipedia infobox tables to populate an existing KB.
Outcome: The proposed method improves accuracy and completeness of the final KB significantly compared to DBpedia's baseline method.
Community lexical access for an endangered polysynthetic language: An electronic dictionary for St. Lawrence Island Yupik (N19-4)

Copied to clipboard

Challenge: a new electronic dictionary for St. Lawrence Island Yupik is developed to facilitate language-learning on the island . the endangered language is spoken primarily on St. lisa's St.liss island, Alaska .
Approach: They propose a morphologically-aware electronic dictionary for St. Lawrence Island Yupik . the dictionary is set in an uncluttered interface and uses HTML, Javascript, and CSS .
Outcome: The proposed dictionary is set in an uncluttered interface and is available in English and in Yupik . it is based on the morphologically-aware version of the Badten et al. paper dictionary .
Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions (2024.acl-demos)

Copied to clipboard

Challenge: Fundus is a news scraper that extracts news articles from the web with just a few lines of code.
Approach: They introduce Fundus, a news scraper that enables users to obtain news articles with just a few lines of code.
Outcome: The proposed news scraper optimizes for quality and provides a unified interface for newspapers.
Leveraging HTML in Free Text Web Named Entity Recognition (2020.coling-main)

Copied to clipboard

Challenge: Named Entity Recognition (NER) is the identification of the proper names of objects.
Approach: They compare HTML tags discarded in free text Named Entity Recognition from Web pages . they find an increased F1 performance for Text+Tags of between 0.9% and 13.2% .
Outcome: The proposed method improves F1 performance over datasets, variants and models.
Paper2Web: Let’s Make Your Paper Alive! (2026.acl-demo)

Copied to clipboard

Challenge: Current approaches to creating layout-aware, interactive academic webpages are limited.
Approach: They propose a benchmark dataset and evaluation framework for assessing academic webpage generation that incorporates rule-based metrics like Connectivity, Completeness and PaperQuiz.
Outcome: The proposed framework outperforms baselines like template-based webpages and arXiv/alphaXivo versions while maintaining low cost.
Semantic Outlier Removal with Embedding Models and LLMs (2025.acl-industry)

Copied to clipboard

Challenge: Modern text processing pipelines require robust methods to remove extraneous content while preserving a document’s core message.
Approach: They propose a method that leverages multilingual sentence embeddings and approximate nearest-neighbor search to identify and excise unwanted text segments.
Outcome: Experiments on HTML datasets show that SORE outperforms structural methods and yields high precision in diverse scenarios.
Supervised and Unsupervised Methods for Robust Separation of Section Titles and Prose Text in Web Documents (D18-1)

Copied to clipboard

Challenge: a web text structure is underutilized, but its visual organization is useful for NLP tasks . a flexible system for extracting hierarchical section titles and prose organization is developed .
Approach: a new system extracts hierarchical section titles and prose organization from web documents . the system uses features from syntax, semantics, discourse and markup to build two models .
Outcome: a new system extracts the hierarchical section titles and prose organization of web documents . the system achieves an overall precision of 0.82 and a recall of 0.98 on three domains of web text .
Transc&Anno: A Graphical Tool for the Transcription and On-the-Fly Annotation of Handwritten Documents (L18-1)

Copied to clipboard

Challenge: Transc&Anno is a web-based collaboration tool for linguists to facilitate the transcription of text images and their shallow on-the-fly annotation.
Approach: They propose a web-based collaboration tool that allows the transcription of text images and their shallow on-the-fly annotation.
Outcome: The Transc&Anno tool can be used for any type of corpora requiring transcription and shallow on-the-fly annotation resulting in inline XML.
A Corpus of Spanish Political Speeches from 1937 to 2019 (2020.lrec-1)

Copied to clipboard

Challenge: a corpus of political speeches in Spanish is documented from 1937 to 2019 . the corpus contains the speeches delivered by the head of state of Spain on Christmas Eve .
Approach: They propose to collect political speeches from the Christmas Eve national speeches from 1937 to 2019 . they propose a Python interface that allows querying and analyzing the corpus .
Outcome: The proposed corpus contains speeches delivered by the king of Spain from 1937 to 2019 . the documents reflect some of the most significant events and political changes in recent history . a set of HTML visualizations is provided to navigate the corpus and explore differences between TF-IDF frequencies.
TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages (2022.naacl-main)

Copied to clipboard

Challenge: Existing models for structural reading comprehension (SRC) only focus on comprehension of plain text, tables, tables or knowledge bases.
Approach: They propose a topological information enhanced model which transforms a token-level task into a tag-level one by introducing a two-stage process.
Outcome: The proposed model outperforms baselines and achieves state-of-the-art performance on the web-based SRC benchmark WebSRC at the time of writing.
AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation (2024.emnlp-main)

Copied to clipboard

Challenge: Existing methods for web scraping suffer from limited adaptability and scalability when faced with a new website.
Approach: They propose a framework that generates web scrapers with large language models and a new executability metric to measure the performance of web scraper generation tasks.
Outcome: The proposed framework can handle diverse web environments more efficiently.
Understanding HTML with Large Language Models (2023.findings-emnlp)

Copied to clipboard

Challenge: Large language models have shown exceptional performance on a variety of natural language tasks, but their capabilities for HTML understanding have not been fully explored.
Approach: They propose to use HTML understanding models to parse HTML and perform HTML navigation tasks with a large-scale HTML dataset.
Outcome: The proposed models perform 50% more tasks with 192x less data than the previous best supervised model.
TurkingBench: A Challenge Benchmark for Web Agents (2025.naacl-long)

Copied to clipboard

Challenge: TurkingBench is a benchmark consisting of tasks presented as web pages with textual instructions and multi-modal contexts.
Approach: They propose to use HTML pages to perform various annotation tasks on crowdsourcing platforms.
Outcome: The proposed model outperforms other models on the TurkingBench benchmark.
Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pre-training (2026.findings-eacl)

Copied to clipboard

Challenge: Existing open-source datasets predominantly apply a single fixed extractor to all webpages.
Approach: They propose to take a Union over different extractors to improve model performance . they show that extractor choice can significantly impact downstream task performance based on content type .
Outcome: The proposed approach can increase the token yield of DCLM-Baseline by 71% while maintaining benchmark performance.
Spanish HPSG Treebank based on the AnCora Corpus (L18-1)

Copied to clipboard

Challenge: a corpus of HPSG annotated trees for Spanish contains morphosyntactic information, annotations for semantic roles, clitic pronouns and relative clauses.
Approach: They propose to build a Spanish HPSG annotated corpus based on the Spanish corpus AnCora and an HTML format for visualizing the trees in a browser.
Outcome: The proposed corpus contains syntactic and morphological information, semantic roles, clitic pronouns and relative clauses, and has CFG style annotations.
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)

Copied to clipboard

Challenge: Existing benchmarks for large language models focus on simple, flat table structures.
Approach: They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG.
Outcome: The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG.
The slurk Interaction Server Framework: Better Data for Better Dialog Models (2022.lrec-1)

Copied to clipboard

Challenge: slurk is a lightweight dialog data collection and testing tool for crowdsourcing platforms.
Approach: They present a lightweight dialog server that allows to set up dialog data collections and run experiments.
Outcome: The slurk software allows to set up dialog data collections and run experiments with no limitations on the number of participants.
HTML: Hierarchical Topology Multi-task Learning for Semantic Parsing in Knowledge Base Question Answering (2025.findings-acl)

Copied to clipboard

Challenge: Existing approaches struggle with mapping questions to precise logical forms . Existing frameworks struggle with complex mapping of questions to logical form .
Approach: They propose a framework that leverages a hierarchical multi-task learning paradigm to enhance the performance of logical form generation.
Outcome: The proposed framework outperforms supervised fine-tuning methods and training-free ones on large language models.
Multimodal Table Understanding (2024.acl-long)

Copied to clipboard

Challenge: Existing approaches to understanding tables rely on textual inputs and table images are difficult to access in real-world scenarios.
Approach: They propose a multimodal table understanding problem where the model needs to generate correct responses to various table-related requests based on the given table image.
Outcome: The proposed model outperforms open-source MLLMs on 23 benchmarks under held-in and held-out settings.
SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents (2024.acl-long)

Copied to clipboard

Challenge: Existing GUI agents interact with the environment through extracted structured data, which can be notably lengthy (e.g., HTML) and occasionally inaccessible (e-book).
Approach: They propose to enhance SeeClick with GUI grounding pre-training and devise a method to automate curation of GUI ground data.
Outcome: The proposed agent improves ScreenSpot, the first realistic GUI grounding benchmark that encompasses mobile, desktop, and web environments.
WikiDragon: A Java Framework For Diachronic Content And Network Analysis Of MediaWikis (L18-1)

Copied to clipboard

Challenge: WikiDragon is a Java Framework designed to give developers in computational linguistics an intuitive API to build, parse and analyze instances of MediaWikis.
Approach: They introduce WikiDragon, a Java Framework that allows developers to build, parse and analyze instances of MediaWikis on their computers.
Outcome: The framework is based on the Wikipedia, Wiktionary, WikiSource or WikiNews and evaluates link extraction, diachronic network analysis and the impact of different frameworks to text analysis.
LongTableBench: Benchmarking Long-Context Table Reasoning across Real-World Formats and Domains (2025.findings-emnlp)

Copied to clipboard

Challenge: Evaluating 52 LLMs reveals that only the strongest models maintain robust performance under increasing context lengths and format diversity.
Approach: They propose a benchmark for evaluating long-context reasoning over semi-structured tables across diverse formats, tasks, and domains.
Outcome: The proposed model outperforms compression-based approaches on tasks requiring semantic integration.
Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation (2025.acl-long)

Copied to clipboard

Challenge: Vision-language models struggle to understand text-rich images due to the scarcity of diverse text-only large language data.
Approach: They propose a framework that leverages the coding capabilities of text-only large language models to create synthetic text-rich multimodal data.
Outcome: The proposed framework can generate high-quality instruction-tuning data using Python, HTML, LaTeX and other languages.
Natural Language Processing Pipeline to Annotate Bulgarian Legislative Documents (2020.lrec-1)

Copied to clipboard

Challenge: The Bulgarian MARCELL corpus consists of 25,283 documents, which are classified into eleven types.
Approach: They present the Bulgarian MARCELL corpus, part of a newly developed multilingual corpus representing the national legislation in seven European countries.
Outcome: The proposed corpus represents the national legislation in seven European countries and the NLP pipeline that turns the web crawled data into structured, linguistically annotated dataset.
Aria-UI: Visual Grounding for GUI Instructions (2025.findings-acl)

Copied to clipboard

Challenge: Using a multimodal model, GUI agents can ground from language instructions to target elements . relying on HTML or AXTree inputs is a challenge for GUI agents .
Approach: They propose a large multimodal model specifically designed for GUI grounding that adopts a pure vision approach instead of auxiliary inputs.
Outcome: The proposed model outperforms vision-only and AXTree-reliant models on offline and online agents.
Prompting for Numerical Sequences: A Case Study on Market Comment Generation (2024.lrec-main)

Copied to clipboard

Challenge: Large language models have been applied to data-to-text generation tasks, but their effectiveness is limited to tasks where the input data is structured and their components are represented as words.
Approach: They propose to use large language models to generate text from numerical sequences.
Outcome: The proposed models perform better than natural languages and longer formats, while resembling natural languages yield less effective results.
WAFFLE: Fine-tuning Multi-Modal Model for Automated Front-End Development (2025.acl-long)

Copied to clipboard

Challenge: Large Language Models (LLMs) have shown promise in generating source code, but two major challenges persist in UI-to-HTML code generation: (1) effectively representing HTML’s hierarchical structure for LLMs; and (2) bridging the gap between the visual nature of UI designs and the text-based format of HTML code.
Approach: They propose a structure-aware attention mechanism that uses a contrastive fine-tuning approach to align LLMs’ understanding of UI images and HTML code.
Outcome: The proposed model outperforms existing methods on the WebSight-Test and Design2Code benchmarks.
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation (2025.emnlp-main)

Copied to clipboard

Challenge: Existing benchmarks focus on specific aspects of web tasks but lack comprehensive coverage.
Approach: They propose a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing involving HTML/CSS/JavaScript, and (3) mockup-to-code generation.
Outcome: The proposed model performs well on basic information extraction, but struggles with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content.
Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework (2026.acl-long)

Copied to clipboard

Challenge: Recent advances in large language models have demonstrated strong potential for understanding user intent . paper describes system architecture, agent roles, retrieval and scoring methods, knowledge graph schema, and evaluation interfaces .
Approach: They propose a multi-agent research discovery and analysis system that integrates multiple agents to reduce the effort required to find, assess, organize, and understand academic literature.
Outcome: The proposed system reduces the effort required to find, assess, organize, and understand academic literature.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations