Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering

Autor:	Lerner, Paul, Ferret, Olivier, Guinaudeau, Camille
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Information Retrieval Computer Science - Machine Learning Computer Science - Multimedia
Druh dokumentu:	Working Paper
Popis:	We present a new pre-training method, Multimodal Inverse Cloze Task, for Knowledge-based Visual Question Answering about named Entities (KVQAE). KVQAE is a recently introduced task that consists in answering questions about named entities grounded in a visual context using a Knowledge Base. Therefore, the interaction between the modalities is paramount to retrieve information and must be captured with complex fusion models. As these models require a lot of training data, we design this pre-training task from existing work in textual Question Answering. It consists in considering a sentence as a pseudo-question and its context as a pseudo-relevant passage and is extended by considering images near texts in multimodal documents. Our method is applicable to different neural network architectures and leads to a 9% relative-MRR and 15% relative-F1 gain for retrieval and reading comprehension, respectively, over a no-pre-training baseline. Comment: Accepted at ECIR 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.04366 Zobrazit plný text záznamu View this record from Arxiv