mlsanityData to 7 Oct 2026

RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing

arXiv 2109.10410 ↗First posted 21 Sep 2021cs.CL cs.IR cs.LGcited by 15

On the map: Retrieval-augmented generation → Generation and context integration

Abstract (arXiv)

While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory has a number of benefits from accuracy improvements to data efficiency for knowledge-focused tasks, such as question answering. In this paper, we are applying retrieval-based modeling ideas to the problem of multi-domain task-oriented semantic parsing for conversational assistants. Our approach, RetroNLU, extends a sequence-to-sequence model architecture with a retrieval component, used to fetch existing similar examples and provide them as an additional input to the model. In particular, we analyze two settings, where we augment an input with (a) retrieved nearest neighbor utterances (utterance-nn), and (b) ground-truth semantic parses of nearest neighbor utterances (semparse-nn). Our technique outperforms the baseline method by 1.5% absolute macro-F1, especially at the low resource setting, matching the baseline model accuracy with only 40% of the data. Furthermore, we analyze the nearest neighbor retrieval component's quality, model sensitivity and break down the performance for semantic parses of different utterance complexity.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation. Builds on 35 of its 50 references that are in the map. First posted on arXiv 21 Sep 2021.

Builds on

35 of 50 references.

Closest ancestors · 2

Same direction, newest first

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks22 May
  2. 2020REALM: Retrieval-Augmented Language Model Pre-Training10 Feb

Other references · 33

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21 more
  1. 2020MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmarkcited by 156
  2. 2018Semantic Parsing for Task Oriented Dialog using Hierarchical Representationscited by 101
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  4. 2020DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialoguecited by 89
  5. 2018Retrieve and Refine: Improved Sequence Generation Models For Dialoguecited by 82
  6. 2020Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsingcited by 72
  7. 2020Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsingcited by 59
  8. 2020Cross-lingual Retrieval for Iterative Self-Supervised Trainingcited by 54
  9. 2020Augmenting Transformers with KNN-Based Composite Memory for Dialoguecited by 53
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  17. 2021Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsingcited by 18
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  19. 2020Multilingual Neural RST Discourse Parsingcited by 13
  20. 2018Knowledge-Aware Conversational Semantic Parsing Over Web Tablescited by 6
  21. 2021Diagnosing Transformers in Task-Oriented Semantic Parsingcited by 5

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Citing papers per year: 2021: 1; 2022: 7; 2023: 4; 2024: 1; 2025: 2.

15 papers.

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  1. 2024Retrieval-Augmented Semantic Parsing: Improving Generalization with Lexical Knowledge
  2. 2023BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer
  3. 2024RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing
  4. 2023Surface-Based Retrieval Reduces Perplexity of Retrieval-Augmented Language Models
  5. 2022kNN-Prompt: Nearest Neighbor Zero-Shot Inference
  6. 2024Reliable, Adaptable, and Attributable Language Models with Retrieval
  7. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Context
  8. 2021Improving language models by retrieving from trillions of tokens