</> Code with Papers by Syntology

Home › Datasets › task › Graph-to-Sequence

Graph-to-Sequence datasets

archive 2025-07-28

4 datasets carry the task tag "Graph-to-Sequence" (the task itself: Graph-to-Sequence), ordered by the archive's paper count. Page 1 of 1: 4 shown of 4. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

All 12,172 Question Answering 413Semantic Segmentation 347Object Detection 332Image Classification 283Classification 222Text Classification 167Language Modelling 166Text Generation 162Visual Question Answering (VQA) 144Named Entity Recognition (NER) 130Pose Estimation 124Anomaly Detection 119Action Recognition 115Instance Segmentation 111Sentiment Analysis 104Text Summarization 98Domain Adaptation 96Speech Recognition 96Reading Comprehension 95Information Retrieval 932D Object Detection 92Image Retrieval 87Machine Translation 82Relation Extraction 822D Semantic Segmentation 81Natural Language Inference 81Image Captioning 79Image Generation 79Depth Estimation 77Node Classification 75Natural Language Understanding 72Autonomous Driving 70Code Generation 70Object Tracking 693D Object Detection 67Face Recognition 67Link Prediction 67Person Re-Identification 65Data Augmentation 63Multi-Task Learning 593D Reconstruction 58Common Sense Reasoning 56Recommendation Systems 56Video Understanding 56Optical Character Recognition (OCR) 55Graph Classification 54Abstractive Text Summarization 52Emotion Recognition 52Medical Image Segmentation 52Word Embeddings 52Graph-to-Sequence 4

Graph-to-Sequence datasets 1–4 of 4

WebNLG
The WebNLG corpus comprises of sets of triplets describing facts (entities and relations between them) and the corresponding facts in form of natural language text.
TextsRelation ExtractionData-to-Text GenerationJoint Entity and Relation ExtractionKG-to-Text GenerationTable-to-Text GenerationUnsupervised KG-to-Text Generation+2 tasks
149 papers · 17 benchmarks
EventNarrative
EventNarrative is a knowledge graph-to-text dataset from publicly available open-world knowledge graphs.
Event ExtractionKG-to-Text GenerationGraph-to-Sequence
3 papers · 1 benchmark
ENT-DESC
ENT-DESC involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter…
Text GenerationKnowledge GraphsKG-to-Text GenerationGraph-to-Sequence
1 paper · 1 benchmark
WikiOFGraph (Wikipedia Ontology-Free Graph-Text)
a high-level explanation of the dataset characteristics We introduce WikiOFGraph, a novel large-scale, domain-diverse dataset synthesized by LLMs, ensuring superior graph-text consistency to advance general-domain graph-to-text generation.
TextsGraphsData-to-Text GenerationGraph-to-Sequence
1 paper · 1 benchmark

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.