mlsanityData to 14 Oct 2026

A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation

arXiv 1805.06553 ↗First posted 16 May 2018cs.CLcited by 20

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Abstract (arXiv)

Natural language generation lies at the core of generative dialogue systems and conversational agents. We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model. We test the model on three datasets in the restaurant, TV and laptop domains, and report both objective and subjective evaluations of our best model. Using a range of automatic metrics, as well as human evaluators, we show that our approach achieves better results than state-of-the-art models on the same datasets.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 May 2018.

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Citing papers per year: 2021: 8; 2022: 2; 2023: 4; 2024: 3; 2025: 2; 2026: 1.

20 papers.

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  1. 2023DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineercited by 57
  2. 2021Text-to-Table: A New Way of Information Extractioncited by 47
  3. 2021Controlling Hallucinations at Word Level in Data-to-Text Generationcited by 41
  4. 2021Neural Data-to-Text Generation with LM-based Text Augmentationcited by 22
  5. 2023Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learningcited by 14
  6. 2021Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labelingcited by 11
  7. 2021Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLGcited by 11
  8. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  9. 2021Search and Learn: Improving Semantic Coverage for Data-to-Text Generationcited by 8
  10. 2021AGGGEN: Ordering and Aggregating while Generatingcited by 7
  11. 2022Scientometric Review of Artificial Intelligence for Operations & Maintenance of Wind Turbines: The Past, Present and Futurecited by 4
  12. 2023Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Rankingcited by 4
8 more
  1. 2025OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMscited by 4
  2. 2024An Extensive Evaluation of Factual Consistency in Large Language Models for Data-to-Text Generationcited by 2
  3. 2021Transforming Multi-Conditioned Generation from Meaning Representationcited by 1
  4. 2024Controllable Decontextualization of Yes/No Question and Answers into Factual Statementscited by 1
  5. 2024Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigationcited by 1
  6. 2023Athena 2.0: Discourse and User Modeling in Open Domain Dialoguecited by 0
  7. 2025Factual Inconsistency in Data-to-Text Generation Scales Exponentially with LLM Size: A Statistical Validationcited by 0
  8. 2026VERITYGATE: A Four-Gate Schema-Level Faithfulness Framework and Paired Benchmark for Grounded LLM Narrations over Structured Evidencecited by 0