Multi-Modal Experience Inspired AI Creation

TL;DR

This paper introduces a novel multi-modal, sequential AI creation framework inspired by human experiences, utilizing a multi-channel sequence-to-sequence model with attention and curriculum negative sampling, validated on a new dataset.

Contribution

It proposes a new multi-modal, sequential AI creation task, along with a multi-channel architecture, a curriculum negative sampling strategy, and a new dataset for benchmarking.

Findings

Significant improvements over baselines in automatic metrics

Effective modeling of multi-modal sequential information

Validated on a newly labeled multi-modal experience dataset

Abstract

AI creation, such as poem or lyrics generation, has attracted increasing attention from both industry and academic communities, with many promising models proposed in the past few years. Existing methods usually estimate the outputs based on single and independent visual or textual information. However, in reality, humans usually make creations according to their experiences, which may involve different modalities and be sequentially correlated. To model such human capabilities, in this paper, we define and solve a novel AI creation problem based on human experiences. More specifically, we study how to generate texts based on sequential multi-modal information. Compared with the previous works, this task is much more difficult because the designed model has to well understand and adapt the semantics among different modalities and effectively convert them into the output in a sequential manner. To alleviate these difficulties, we firstly design a multi-channel sequence-to-sequence architecture equipped with a multi-modal attention network. For more effective optimization, we then propose a curriculum negative sampling strategy tailored for the sequential inputs. To benchmark this problem and demonstrate the effectiveness of our model, we manually labeled a new multi-modal experience dataset. With this dataset, we conduct extensive experiments by comparing our model with a series of representative baselines, where we can demonstrate significant improvements in our model based on both automatic and human-centered metrics. The code and data are available at: \url{https://github.com/Aman-4-Real/MMTG}.