mlsanityData to 7 Oct 2026

Language Model is All You Need: Natural Language Understanding as Question Answering

arXiv 2011.03023 ↗First posted 5 Nov 2020cs.CL cs.AIcited by 29

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Abstract (arXiv)

Different flavors of transfer learning have shown tremendous impact in advancing research and applications of machine learning. In this work we study the use of a specific family of transfer learning, where the target domain is mapped to the source domain. Specifically we map Natural Language Understanding (NLU) problems to QuestionAnswering (QA) problems and we show that in low data regimes this approach offers significant improvements compared to other approaches to NLU. Moreover we show that these gains could be increased through sequential transfer learning across NLU problems from different domains. We show that our approach could reduce the amount of required data for the same performance by up to a factor of 10.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Nov 2020.

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Citing papers per year: 2021: 5; 2022: 9; 2023: 10; 2024: 3; 2025: 1; 2026: 1.

29 papers.

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  1. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  2. 2021Few-Shot Bot: Prompt-Based Learning for Dialogue Systemscited by 64
  3. 2021Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Trackingcited by 61
  4. 2022Description-Driven Task-Oriented Dialog Modelingcited by 50
  5. 2023ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languagescited by 33
  6. 2023Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Informationcited by 24
  7. 2024Can LLMs Master Math? Investigating Large Language Models on Math Stack Exchangecited by 22
  8. 2022Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformerscited by 20
  9. 2021Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systemscited by 16
  10. 2022SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialoguescited by 16
  11. 2022Robust Dialogue State Tracking with Weak Supervision and Sparse Datacited by 15
  12. 2022MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialoguecited by 15
17 more
  1. 2022Towards Textual Out-of-Domain Detection without In-Domain Labelscited by 14
  2. 2023Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streamscited by 13
  3. 2024gTBLS: Generating Tables from Text by Conditional Question Answeringcited by 13
  4. 2022Improved and Efficient Conversational Slot Labeling through Question Answeringcited by 10
  5. 2022Compositional Task-Oriented Parsing as Abstractive Question Answeringcited by 10
  6. 2022NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialoguecited by 8
  7. 2023Transfer-Free Data-Efficient Multilingual Slot Labelingcited by 4
  8. 2023Transferring Visual Attributes from Natural Language to Verified Image Generationcited by 4
  9. 2023Knowledge-Aware Audio-Grounded Generative Slot Filling for Limited Annotated Datacited by 3
  10. 2023EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry Systemcited by 3
  11. 2023SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLUcited by 3
  12. 2023CTRAN: CNN-Transformer-based Network for Natural Language Understandingcited by 2
  13. 2023TWIZ-v2: The Wizard of Multimodal Conversational-Stimuluscited by 2
  14. 2025Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Modelscited by 1
  15. 2021Word frequency-rank relationship in tagged textscited by 0
  16. 2024Towards Zero-Shot, Controllable Dialog Planning with LLMscited by 0
  17. 2026DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisitioncited by 0