mlsanityData to 7 Oct 2026

Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System

arXiv 2205.04006 ↗First posted 9 May 2022cs.CL cs.AIcited by 16

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Abstract (arXiv)

Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. Our goal is to build Artificial Intelligence (AI) systems that can assist children in their learning process. Within such complex frameworks, Spoken Dialogue Systems (SDS) are crucial building blocks to handle efficient task-oriented communication with children in game-based learning settings. We are working towards a multimodal dialogue system for younger kids learning basic math concepts. Our focus is on improving the Natural Language Understanding (NLU) module of the task-oriented SDS pipeline with limited datasets. This work explores the potential benefits of data augmentation with paraphrase generation for the NLU models trained on small task-specific datasets. We also investigate the effects of extracting entities for conceivably further data expansion. We have shown that paraphrasing with model-in-the-loop (MITL) strategies using small seed data is a promising approach yielding improved performance results for the Intent Recognition task.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 42 of its 80 references that are in the map. First posted on arXiv 9 May 2022.

Builds on

42 of 80 references.

References · 42

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30 more
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Citing papers per year: 2022: 3; 2023: 5; 2024: 4; 2025: 4.

16 papers.

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  1. 2023Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Informationcited by 24
  2. 2023A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Textscited by 23
  3. 2025Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasingcited by 14
  4. 2022Privacy-Preserving Domain Adaptation of Semantic Parserscited by 12
  5. 2024Data Generation Using Large Language Models for Text Classification: An Empirical Case Studycited by 11
  6. 2023FaceChat: An Emotion-Aware Face-to-face Dialogue Frameworkcited by 3
  7. 2024PGA-SciRE: Harnessing LLM on Data Augmentation for Enhancing Scientific Relation Extractioncited by 3
  8. 2022NLU for Game-based Learning in Real: Initial Evaluationscited by 2
  9. 2022End-to-End Evaluation of a Spoken Dialogue System for Learning Basic Mathematicscited by 2
  10. 2024On Evaluation Protocols for Data Augmentation in a Limited Data Scenariocited by 2
  11. 2025Concept Extraction for Time Series with ECLAD-tscited by 2
  12. 2025SPADE: Structured Prompting Augmentation for Dialogue Enhancement in Machine-Generated Text Detectioncited by 1
4 more
  1. 2023Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Homecited by 0
  2. 2023Automatic Translation of Hate Speech to Non-hate Speech in Social Media Textscited by 0
  3. 2024Action Controlled Paraphrasingcited by 0
  4. 2025SMCLM: Semantically Meaningful Causal Language Modeling for Autoregressive Paraphrase Generationcited by 0