Sort Story: Sorting Jumbled Images and Captions into Stories (1606.07493v5)

Abstract: Temporal common sense has applications in AI tasks such as QA, multi-document summarization, and human-AI communication. We propose the task of sequencing -- given a jumbled set of aligned image-caption pairs that belong to a story, the task is to sort them such that the output sequence forms a coherent story. We present multiple approaches, via unary (position) and pairwise (order) predictions, and their ensemble-based combinations, achieving strong results on this task. We use both text-based and image-based features, which depict complementary improvements. Using qualitative examples, we demonstrate that our models have learnt interesting aspects of temporal common sense.

• The paper introduces innovative unary and pairwise models that predict the optimal positions of story elements from jumbled image-caption pairs.
• It employs an ensemble voting scheme that significantly improves ordering accuracy as measured by metrics such as Spearman's correlation and pairwise accuracy.
• The approach enhances AI's temporal common sense and paves the way for applications in multi-document summarization, question answering, and human-AI communication.

Overview of Sort Story: Sorting Jumbled Images and Captions into Stories

The task of temporal sequencing in multi-modal narratives, proposed in "Sort Story: Sorting Jumbled Images and Captions into Stories," is designed to enhance the understanding of temporal common sense in artificial intelligence systems. This paper presents a novel approach to sort jumbled sequences of image-caption pairs into coherent stories without explicit temporal annotations.

Methods and Models

The authors introduce several models leveraging both unary and pairwise predictions, each aimed at determining either the most suitable position for individual story elements or the relative ordering between pairs of elements. The unary aspect involves position predictions using text-based skip-thought vectors and image-based CNN embeddings applied through neural networks. Key methods are:

• Unary Models: These models predict the likelihood of an element belonging to a certain position within a story. The approach utilizes skip-thought vectors for textual features and the VGG CNN for image features.
• Pairwise Models: Leveraging the relational aspect of elements, pairwise models estimate the relative order of elements in stories. This includes using hinge-loss in neural networks for textual features and learning embedding positions through LSTM networks.

The paper details the construction of scoring functions under both unary and pairwise paradigms and outlines the optimization strategies including using the Hungarian algorithm for unary models and exhaustive search for pairwise models.

Ensemble Method

A significant contribution is the ensemble-based voting scheme. By combining unary models and pairwise models, the approach synergizes complementary information about the temporal storytelling structure to enhance accuracy. The chosen ensemble, based on the results of comprehensive validation, uses voting to consolidate predictions from top model configurations.

Results

Empirical results reflect the efficacy of both unary and pairwise models, with ensemble methods outperforming each individually. The metrics of Spearman's correlation, pairwise accuracy, and average distance are used to gauge performance. The proposed models exhibit improved performance over random ordering, particularly the ensemble method, showcasing robust capability in predicting coherent narrative sequences.

Analysis of Temporal Common Sense

Insightful qualitative analyses illustrate the model's ability to discern elements of narrative structure, such as identifying typical beginning and ending cues in stories. The models appear to grasp the three-act structure, which involves setting up the scene, developing through successive elements, and reaching a conclusion that ties together the narrative threads.

Implications and Future Directions

The work opens avenues for integrating temporal reasoning in AI systems more deeply, with potential applications spanning multi-document summarization, enhanced question answering systems, and enriched human-AI interaction. There are opportunities to further explore model scalability and efficiency when dealing with longer and more complex sequences. Extending the current models to diverse narrative styles and genres could reveal deeper aspects of temporal storytelling relevant for AI understanding scenarios.

In summary, "Sort Story" provides a foundational framework to enable AI systems to comprehend temporal sequences through multi-modal data. As AI seeks to bridge understanding with real-world narratives, these approaches could significantly impact the development of more intuitive and intelligent communication systems. Future work may delve further into optimizing model interpretations and applications across varied domains.