mlsanityData to 14 Oct 2026

Simplifying Semantic Annotations of SMCalFlow

arXiv 2206.13425 ↗First posted 27 Jun 2022cs.CLcited by 7

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Abstract (arXiv)

SMCalFlow is a large corpus of semantically detailed annotations of task-oriented natural dialogues. The annotations use a dataflow approach, in which the annotations are programs which represent user requests. Despite the availability, size and richness of this annotated corpus, it has seen only very limited use in dialogue systems research work, at least in part due to the difficulty in understanding and using the annotations. To address these difficulties, this paper suggests a simplification of the SMCalFlow annotations, as well as releases code needed to inspect the execution of the annotated dataflow programs, which should allow researchers of dialogue systems an easy entry point to experiment with various dataflow based implementations and annotations.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 2 of its 2 references that are in the map. First posted on arXiv 27 Jun 2022.

Builds on

2 of 2 references.

References · 2

  1. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 766
  2. 2020Task-Oriented Dialogue as Dataflow Synthesiscited by 122

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Cited by year

Citing papers per year: 2022: 3; 2023: 4.

7 papers.

  1. 2022Diverse Demonstrations Improve In-context Compositional Generalizationcited by 139
  2. 2023Coverage-based Example Selection for In-Context Learningcited by 59
  3. 2023GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottleneckscited by 6
  4. 2022MultiWOZ-DF -- A Dataflow implementation of the MultiWOZ datasetcited by 1
  5. 2023Few-Shot Adaptation for Parsing Contextual Utterances with LLMscited by 1
  6. 2022DFEE: Interactive DataFlow Execution and Evaluation Kitcited by 0
  7. 2023Dataflow Dialogue Generationcited by 0