Leveraging Customer Feedback for Multi-modal Insight Extraction

TL;DR

A novel multi-modal method is proposed 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 and a weakly-supervised data generation technique that produces training data for this task.

Abstract

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.

Figures (3)

• Figure 1: Multi-modal INsight Extraction (MINE) Architecture
• Figure 2: Product category coverage and precision as a function of raw data threshold for fine-tuning CLIP. The graph shows the trade-off between coverage and precision for different values of the threshold
• Figure 3: Training data threshold selection