Summary of Making Task-oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests, By Amogh Mannekote et al.
Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests
by Amogh Mannekote, Jinseok Nam, Ziming Li, Jian Gao, Kristy Elizabeth Boyer, Bonnie J. Dorr
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a solution to improve natural language understanding and dialogue state tracking in virtual assistants. It focuses on Indirect User Requests (IURs), which are common in human-human task-oriented dialogue, requiring world knowledge and pragmatic reasoning from the listener. Large Language Models (LLMs) can handle these requests effectively, but smaller models deployed on virtual assistants often struggle due to resource constraints. To address this, the authors propose a set of linguistic criteria along with an LLM-based pipeline for generating realistic IURs to test NLU and DST models before deployment in a new domain. The authors 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make virtual assistants smarter by improving how they understand what people are saying. They focus on special requests that aren’t straightforward, like “It’s cold in here” instead of “Can you turn up the heat?” These types of requests require common sense and understanding of everyday situations. The authors developed a system to generate realistic versions of these indirect requests to test how well virtual assistants can understand and respond to them. |
Keywords
» Artificial intelligence » Language understanding » Tracking
