mlsanityData to 7 Oct 2026

ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation

arXiv 1910.12129 ↗First posted 26 Oct 2019cs.CLcited by 31

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Abstract (arXiv)

The uptake of deep learning in natural language generation (NLG) led to the release of both small and relatively large parallel corpora for training neural models. The existing data-to-text datasets are, however, aimed at task-oriented dialogue systems, and often thus limited in diversity and versatility. They are typically crowdsourced, with much of the noise left in them. Moreover, current neural NLG models do not take full advantage of large training data, and due to their strong generalizing properties produce sentences that look template-like regardless. We therefore present a new corpus of 7K samples, which (1) is clean despite being crowdsourced, (2) has utterances of 9 generalizable and conversational dialogue act types, making it more suitable for open-domain dialogue systems, and (3) explores the domain of video games, which is new to dialogue systems despite having excellent potential for supporting rich conversations.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Oct 2019.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 6; 2024: 8; 2025: 7; 2026: 2.

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  1. 2024Representation Noising: A Defence Mechanism Against Harmful Finetuningcited by 93
  2. 2024RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptationcited by 48
  3. 2022GEMv2: Multilingual NLG Benchmarking in a Single Line of Codecited by 36
  4. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  5. 2023DeltaZip: Efficient Serving of Multiple Full-Model-Tuned LLMscited by 17
  6. 2023Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learningcited by 14
  7. 2025HALO: Hadamard-Assisted Lower-Precision Optimization for LLMscited by 14
  8. 2024HiFT: A Hierarchical Full Parameter Fine-Tuning Strategycited by 12
  9. 2021Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLGcited by 11
  10. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  11. 2024Promoting Data and Model Privacy in Federated Learning through Quantized LoRAcited by 8
  12. 2021Jurassic is (almost) All You Need: Few-Shot Meaning-to-Text Generation for Open-Domain Dialoguecited by 6
19 more
  1. 2021Towards objectively evaluating the quality of generated medical summariescited by 5
  2. 2021Modeling Performance in Open-Domain Dialogue with PARADISEcited by 5
  3. 2021Athena 2.0: Contextualized Dialogue Management for an Alexa Prize SocialBotcited by 4
  4. 2023Improving Open-Domain Dialogue Evaluation with a Causal Inference Modelcited by 4
  5. 2023Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Rankingcited by 4
  6. 2024A Systematic Review of Data-to-Text NLGcited by 4
  7. 2024Parameter-Efficient Fine-Tuning via Circular Convolutioncited by 4
  8. 2025LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-Tuningcited by 3
  9. 2025Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuningcited by 3
  10. 2025SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Versioncited by 3
  11. 2023You Can Generate It Again: Data-to-Text Generation with Verification and Correction Promptingcited by 2
  12. 2024An Extensive Evaluation of Factual Consistency in Large Language Models for Data-to-Text Generationcited by 2
  13. 2024Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigationcited by 1
  14. 2025Enhancing Model Privacy in Federated Learning with Random Masking and Quantizationcited by 1
  15. 2023Athena 2.0: Discourse and User Modeling in Open Domain Dialoguecited by 0
  16. 2025Factual Inconsistency in Data-to-Text Generation Scales Exponentially with LLM Size: A Statistical Validationcited by 0
  17. 2025Enhancing Learned Knowledge in LoRA Adapters Through Efficient Contrastive Decoding on Ascend NPUscited by 0
  18. 2026Impact of enriched meaning representations for language generation in dialogue tasks: A comprehensive exploration of the relevance of tasks, corpora and metricscited by 0
  19. 2026Active Learning with Low-Rank Structure for Data Selectioncited by 0