Date of Award
2017
Document Type
Honors Thesis (Open Access)
Department
Colby College. Computer Science Dept.
Advisor(s)
Stephanie Taylor
Abstract
Machine translation is a widely researched topic in the field of Natural Language Processing and most recently, neural network models have been shown to be very effective at this task. The model, called sequence-to-sequence model, learns to map an input sequence in one language to a vector of fixed dimensionality and then map that vector to an output sequence in another language without any human intervention provided that there is enough training data. Focusing on English-French translation, in this paper, I present a way to simplify the learning process by replacing English input sentences by word-by-word translation of those sentences. I found that this approach improves the performance of a sequence-to-sequence model which is 3-layer deep and has a bidirectional LSTM encoder by more than 30% on the same dataset.
Keywords
deep learning, translation, sequence to sequence, neural networks, recurrent neural networks, machine translation
Recommended Citation
Sah, Vivek, "An alternative approach to training Sequence-to-Sequence model for Machine Translation" (2017). Honors Theses. Paper 949.https://digitalcommons.colby.edu/honorstheses/949