Summary of Leveraging Customer Feedback For Multi-modal Insight Extraction, by Sandeep Sricharan Mukku et al.
Leveraging Customer Feedback for Multi-modal Insight Extraction
by Sandeep Sricharan Mukku, Abinesh Kanagarajan, Pushpendu Ghosh, Chetan Aggarwal
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
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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 to extracting relevant customer feedback pairs in text and image modalities is proposed. The method fuses image and text information in a latent space and uses an image-text grounded text decoder to extract actionable insights. A weakly-supervised data generation technique is introduced, enabling the model to outperform existing baselines by 14 points in F1 score on unseen data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to make your product better by listening to customers’ feedback, but it’s hard to find the good stuff among all the comments and images. This paper shows a new way to do that. It combines text and image information into one place and uses special computer code to pick out what’s important. This helps businesses get useful ideas from their customers. |
Keywords
» Artificial intelligence » Decoder » F1 score » Latent space » Supervised
