Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests
Abstract: - Indirect User Requests (IURs), such as "It's cold in here" instead of "Could you please increase the temperature?" are common in human-human task-oriented dialogue and require world knowledge and pragmatic reasoning from the listener. While large language models (LLMs) can handle these requests effectively, smaller models deployed on virtual assistants often struggle due to resource constraints. Moreover, existing task-oriented dialogue benchmarks lack sufficient examples of complex discourse phenomena such as indirectness. To address this, we propose a set of linguistic criteria along with an LLM-based pipeline for generating realistic IURs to test natural language understanding (NLU) and dialogue state tracking (DST) models before deployment in a new domain. We also release IndirectRequests, a dataset of IURs based on the Schema Guided Dialog (SGD) corpus, as a comparative testbed for evaluating the performance of smaller models in handling indirect requests.
Paper Type: Long
Research Area: Dialogue and Interactive Systems
Research Area Keywords: evaluation and metrics, task-oriented, commonsense reasoning, dialogue state tracking, automatic evaluation, NLP datasets
Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Data resources, Data analysis
Languages Studied: English
Submission Number: 554
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