Pre-Trained Word Embedding and Language Model Improve Multimodal Machine Translation: A Case Study in Multi30K-Reference-Cited by-同舟云学术

Pre-Trained Word Embedding and Language Model Improve Multimodal Machine Translation: A Case Study in Multi30K

Author:

Hirasawa Tosho¹^ORCID,Kaneko Masahiro¹^ORCID,Imankulova Aizhan¹,Komachi Mamoru¹^ORCID

Affiliation:

1. Graduate School of System Design, Tokyo Metropolitan University, Hino, Tokyo, Japan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference59 articles.

1. VisualBERT: A simple and performant baseline for vision and language;li;arXiv 1908 03557,2019

2. Unified vision-language pre-training for image captioning and VQA;zhou;Proc AAAI,2019

3. Cross-lingual language model pretraining;lample;Proc NeurIPS,2019

4. Von Mises–Fisher loss for training sequence to sequence models with continuous outputs;kumar;Proc ICLR,2019

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

2. Educational Resource Material Translation System;International Journal of Advanced Research in Science, Communication and Technology;2024-05-04

3. Automatic translation system from Classical Chinese to Modern Chinese based on large language models;International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023);2024-03-27

4. Transfer Learning-Based Neural Machine Translation for Low-Resource Languages;ACM Transactions on Asian and Low-Resource Language Information Processing;2023-09-13