mlsanityData to 7 Oct 2026

Findings of the E2E NLG Challenge

arXiv 1810.01170 ↗First posted 2 Oct 2018cs.CLcited by 28

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Abstract (arXiv)

This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems. Recent end-to-end generation systems are promising since they reduce the need for data annotation. However, they are currently limited to small, delexicalised datasets. The E2E NLG shared task aims to assess whether these novel approaches can generate better-quality output by learning from a dataset containing higher lexical richness, syntactic complexity and diverse discourse phenomena. We compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures -- with the majority implementing sequence-to-sequence models (seq2seq) -- as well as systems based on grammatical rules and templates.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Oct 2018.

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Citing papers per year: 2021: 13; 2022: 5; 2023: 2; 2024: 4; 2025: 3; 2026: 1.

28 papers.

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  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  2. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
  3. 2021NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristicscited by 153
  4. 2021Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Groundingcited by 106
  5. 2023Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspectioncited by 44
  6. 2021Structural Information Preserving for Graph-to-Text Generationcited by 24
  7. 2022The Authenticity Gap in Human Evaluationcited by 24
  8. 2021Underreporting of errors in NLG output, and what to do about itcited by 19
  9. 2021Improving Compositional Generalization with Self-Training for Data-to-Text Generationcited by 19
  10. 2021Learning to Reason for Text Generation from Scientific Tablescited by 13
  11. 2021Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLGcited by 11
  12. 2021Style Control for Schema-Guided Natural Language Generationcited by 10
16 more
  1. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  2. 2021Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Modelscited by 9
  3. 2021SAPPHIRE: Approaches for Enhanced Concept-to-Text Generationcited by 8
  4. 2023MERGE: Fast Private Text Generationcited by 8
  5. 2022Non-neural Models Matter: A Re-evaluation of Neural Referring Expression Generation Systemscited by 7
  6. 2024Generative Subgraph Retrieval for Knowledge Graph-Grounded Dialog Generationcited by 6
  7. 2024Better than Random: Reliable NLG Human Evaluation with Constrained Active Samplingcited by 4
  8. 2021Towards transparency in NLP shared taskscited by 3
  9. 2025SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Versioncited by 3
  10. 2021Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisationcited by 1
  11. 2024Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigationcited by 1
  12. 2022Comparing Computational Architectures for Automated Journalismcited by 0
  13. 2024Self-training from Self-memory in Data-to-text Generationcited by 0
  14. 2025Evaluating and Improving Graph to Text Generation with Large Language Modelscited by 0
  15. 2025Factual Inconsistency in Data-to-Text Generation Scales Exponentially with LLM Size: A Statistical Validationcited by 0
  16. 2026Cross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Datacited by 0