arXiv:2004.04902
An In-depth Walkthrough on Evolution of Neural Machine Translation
원문 초록
Neural Machine Translation (NMT) methodologies have burgeoned from using simple feed-forward architectures to the state of the art; viz. BERT model. The use cases of NMT models have been broadened from just language translations to conversational agents (chatbots), abstractive text summarization, image captioning, etc. which have proved to be a gem in their respective applications. This paper aims to study the major trends in Neural Machine Translation, the state of the art models in the domain and a high level comparison between them.
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Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translation
A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting
Do Not Change Me: On Transferring Entities Without Modification in Neural Machine Translation -- a Multilingual Perspective
Calibrating Translation Decoding with Quality Estimation on LLMs
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation
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