Summary of Synthesizing Conversations From Unlabeled Documents Using Automatic Response Segmentation, by Fanyou Wu et al.
Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation
by Fanyou Wu, Weijie Xu, Chandan K. Reddy, Srinivasan H. Sengamedu
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 A novel approach is proposed to tackle the challenge of inadequate training data in conversational question answering (ConvQA) systems. The method focuses on creating a dialogue system that allows users to comprehend internal documents from enterprises, rather than relying on searching engines. A robust dialog synthesising method is developed, which learns segmentation for the dialog task instead of using sentence boundaries. The generated synthetic dataset outperforms WikiDialog in machine and human evaluations. By employing inpainted data for ConvQA retrieval system pre-training, a notable improvement in performance across OR-QuAC benchmarks is observed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists are trying to solve a big problem with machines that answer questions by talking. Right now, these machines need lots of training data, which can be hard and expensive to get. The researchers came up with an idea to use the vast amount of internal documents from companies to create a conversation system. This would allow people to easily understand these documents without having to search for information. They developed a new way to generate high-quality synthetic data that beats existing methods in tests. By using this new approach, they were able to make machines better at answering questions. |
Keywords
» Artificial intelligence » Question answering » Synthetic data
