CLJun 27, 2022

Simplifying Semantic Annotations of SMCalFlow

arXiv:2206.13425v1586 citationsh-index: 4Has Code
Originality Synthesis-oriented
AI Analysis

This work addresses the difficulty for dialogue systems researchers in utilizing SMCalFlow, offering an incremental improvement to enhance usability.

The paper tackles the limited use of SMCalFlow, a large annotated corpus for task-oriented dialogues, by proposing a simplification of its complex semantic annotations and releasing code to inspect dataflow program execution, aiming to make it more accessible for dialogue systems research.

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.

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