Leveraging Customer Feedback for Multi-modal Insight Extraction
This addresses a specific business need for processing customer feedback more efficiently, but it is incremental as it builds on existing multi-modal methods.
The paper tackles the problem of extracting actionable text-image pairs from multi-modal customer feedback in a single pass, proposing a novel method that fuses image and text information in a latent space and uses a weakly-supervised data generation technique, resulting in a 14-point F1 score improvement over baselines.
Businesses can benefit from customer feedback in different modalities, such as text and images, to enhance their products and services. However, it is difficult to extract actionable and relevant pairs of text segments and images from customer feedback in a single pass. In this paper, we propose a novel multi-modal method that fuses image and text information in a latent space and decodes it to extract the relevant feedback segments using an image-text grounded text decoder. We also introduce a weakly-supervised data generation technique that produces training data for this task. We evaluate our model on unseen data and demonstrate that it can effectively mine actionable insights from multi-modal customer feedback, outperforming the existing baselines by $14$ points in F1 score.