mlsanityData to 14 Oct 2026

Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems

arXiv 2112.05842 ↗First posted 10 Dec 2021cs.CL cs.LG eess.AScited by 15

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Abstract (arXiv)

As more users across the world are interacting with dialog agents in their daily life, there is a need for better speech understanding that calls for renewed attention to the dynamics between research in automatic speech recognition (ASR) and natural language understanding (NLU). We briefly review these research areas and lay out the current relationship between them. In light of the observations we make in this paper, we argue that (1) NLU should be cognizant of the presence of ASR models being used upstream in a dialog system's pipeline, (2) ASR should be able to learn from errors found in NLU, (3) there is a need for end-to-end datasets that provide semantic annotations on spoken input, (4) there should be stronger collaboration between ASR and NLU research communities.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 22 of its 54 references that are in the map. First posted on arXiv 10 Dec 2021.

Builds on

22 of 54 references.

References · 22

  1. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  2. 2018CoQA: A Conversational Question Answering Challengecited by 683
  3. 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
  4. 2019Speech Model Pre-training for End-to-End Spoken Language Understandingcited by 193
  5. 2018Towards end-to-end spoken language understandingcited by 92
  6. 2018Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehensioncited by 74
  7. 2018From Audio to Semantics: Approaches to end-to-end spoken language understandingcited by 63
  8. 2020On the Comparison of Popular End-to-End Models for Large Scale Speech Recognitioncited by 63
  9. 2018A Survey on Semantic Parsingcited by 62
  10. 2018DRCD: a Chinese Machine Reading Comprehension Datasetcited by 57
  11. 2019Corpora Generation for Grammatical Error Correctioncited by 53
  12. 2020RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systemscited by 36
10 more
  1. 2017Limits of End-to-End Learningcited by 35
  2. 2020Towards Data Distillation for End-to-end Spoken Conversational Question Answeringcited by 35
  3. 2020Robustness Testing of Language Understanding in Task-Oriented Dialogcited by 32
  4. 2020Joint Contextual Modeling for ASR Correction and Language Understandingcited by 30
  5. 2021NoiseQA: Challenge Set Evaluation for User-Centric Question Answeringcited by 25
  6. 2018ODSQA: Open-domain Spoken Question Answering Datasetcited by 21
  7. 2020End-to-End Spoken Language Understanding Without Full Transcriptscited by 19
  8. 2020Data Augmentation for Training Dialog Models Robust to Speech Recognition Errorscited by 17
  9. 2020Improving Spoken Language Understanding By Exploiting ASR N-best Hypothesescited by 12
  10. 2019Investigation of Error Simulation Techniques for Learning Dialog Policies for Conversational Error Recoverycited by 5

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Cited by year (2026 to 14 Oct)

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

15 papers.

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  1. 2022Generative Spoken Dialogue Language Modelingcited by 126
  2. 2022Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systemscited by 28
  3. 2022"Do you follow me?": A Survey of Recent Approaches in Dialogue State Trackingcited by 22
  4. 2024SSR: Alignment-Aware Modality Connector for Speech Language Modelscited by 16
  5. 2025Voila: Voice-Language Foundation Models for Real-Time Autonomous Interaction and Voice Role-Playcited by 10
  6. 2023OLISIA: a Cascade System for Spoken Dialogue State Trackingcited by 5
  7. 2022Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systemscited by 2
  8. 2025Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialectscited by 2
  9. 2023Is one brick enough to break the wall of spoken dialogue state tracking?cited by 1
  10. 2026Credit C-GPT: A Domain-Specialized Large Language Model for Conversational Understanding in Vietnamese Debt Collectioncited by 1
  11. 2026Analyzing Error Propagation in Korean Spoken QA with ASR-LLM Cascadescited by 1
  12. 2023Are cascade dialogue state tracking models speaking out of turn in spoken dialogues?cited by 0
3 more
  1. 2024Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasetscited by 0
  2. 2025Enabling Automatic Self-Talk Detection via Earablescited by 0
  3. 2026CORTIS: Text-Only Adaptation of Spoken Language Models for Task-Oriented Voice Agentscited by 0