Search Results for author:
Found 11 papers, 4 papers with code
Learning to Discriminate Noises for Incorporating External Information in Neural Machine Translation
Previous studies show that incorporating external information could improve the translation quality of Neural Machine Translation (NMT) systems.
Generating Diverse Translation by Manipulating Multi-Head Attention
Experiments also show that back-translation with these diverse translations could bring significant improvement on performance on translation tasks.
Alleviating the Inequality of Attention Heads for Neural Machine Translation
Recent studies show that the attention heads in Transformer are not equal.
Ranked #1 on
Machine Translation
on WMT2017 Turkish-English
Rethinking Document-level Neural Machine Translation
This paper does not aim at introducing a novel model for document-level neural machine translation.
Multilingual Translation via Grafting Pre-trained Language Models
Can pre-trained BERT for one language and GPT for another be glued together to translate texts?
Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Prompts
Domain adaptation is an important challenge for neural machine translation.
Controlling Styles in Neural Machine Translation with Activation Prompt
Controlling styles in neural machine translation (NMT) has attracted wide attention, as it is crucial for enhancing user experience.
Better Datastore, Better Translation: Generating Datastores from Pre-Trained Models for Nearest Neural Machine Translation
The effectiveness of kNNMT directly depends on the quality of retrieved neighbors.
Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation
Therefore, this paper correspondingly establishes new methods and new datasets for MMT.
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation
We also introduce two deliberately designed test sets to verify the necessity of visual information: Ambiguous with the presence of ambiguous words, and Unambiguous in which the text context is self-contained for translation.
Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation
Document-level Neural Machine Translation (DocNMT) has been proven crucial for handling discourse phenomena by introducing document-level context information.
