@inproceedings{3b5881011fd847ad8f7edad2763c4a6c,
title = "Collaborative Regularization for Bidirectional Domain Mapping",
abstract = "Learning both domain mapping and domain knowledge is crucial for different sequence-to-sequence (seq2seq) tasks. Traditionally, seq2seq model only characterized domain mapping while the knowledge in source and target domains was ignored. To strengthen seq2seq representation, this study presents a unified transformer for bidirectional domain mapping where collaborative regularization is imposed. This regularization enforces the bidirectional mapping constraint and avoids the model from overfitting for better generalization. Importantly, the unified learning objective is optimized for collaborative learning among different modules in two domains with two learning directions. Experiments on machine translation demonstrate the merit of unified transformer by comparing with the existing methods under different tasks and settings.",
keywords = "Sequential learning, domain mapping, machine translation, sequence-to-sequence learning, transformer",
author = "Chien, {Jen Tzung} and Chang, {Wei Hsiang}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Joint Conference on Neural Networks, IJCNN 2021 ; Conference date: 18-07-2021 Through 22-07-2021",
year = "2021",
month = jul,
day = "18",
doi = "10.1109/IJCNN52387.2021.9533466",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings",
address = "美國",
}