mlsanityData to 7 Oct 2026

Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings

arXiv 1606.05491 ↗First posted 17 Jun 2016cs.CLcited by 27

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Abstract (arXiv)

We present a natural language generator based on the sequence-to-sequence approach that can be trained to produce natural language strings as well as deep syntax dependency trees from input dialogue acts, and we use it to directly compare two-step generation with separate sentence planning and surface realization stages to a joint, one-step approach. We were able to train both setups successfully using very little training data. The joint setup offers better performance, surpassing state-of-the-art with regards to n-gram-based scores while providing more relevant outputs.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jun 2016.

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Citing papers per year: 2021: 14; 2022: 7; 2023: 3; 2024: 2; 2025: 0; 2026: 1.

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  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  2. 2021The GEM Benchmark: Natural Language Generation, its Evaluation and Metricscited by 239
  3. 2021NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristicscited by 153
  4. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  5. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 89
  6. 2022CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learningcited by 33
  7. 2022Context-Aware Language Modeling for Goal-Oriented Dialogue Systemscited by 22
  8. 2021AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentationcited by 17
  9. 2021Controllable and Diverse Text Generation in E-commercecited by 13
  10. 2024Unifying Structured Data as Graph for Data-to-Text Pre-Trainingcited by 13
  11. 2021Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labelingcited by 11
  12. 2021Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLGcited by 11
15 more
  1. 2021GenNI: Human-AI Collaboration for Data-Backed Text Generationcited by 11
  2. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  3. 2024HyperSDFusion: Bridging Hierarchical Structures in Language and Geometry for Enhanced 3D Text2Shape Generationcited by 8
  4. 2021AGGGEN: Ordering and Aggregating while Generatingcited by 7
  5. 2021Incremental Beam Manipulation for Natural Language Generationcited by 6
  6. 2021Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillationcited by 5
  7. 2021Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategycited by 4
  8. 2021Document Automation Architectures and Technologies: A Surveycited by 3
  9. 2022RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generatorscited by 3
  10. 2021Transforming Multi-Conditioned Generation from Meaning Representationcited by 1
  11. 2023Document Automation Architectures: Updated Survey in Light of Large Language Modelscited by 1
  12. 2022E2E Refined Datasetcited by 0
  13. 2022Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systemscited by 0
  14. 2023Exposing the Functionalities of Neurons for Gated Recurrent Unit Based Sequence-to-Sequence Modelcited by 0
  15. 2026VERITYGATE: A Four-Gate Schema-Level Faithfulness Framework and Paired Benchmark for Grounded LLM Narrations over Structured Evidencecited by 0