Search Results for author:
Found 11 papers, 2 papers with code
The NiuTrans Machine Translation Systems for WMT20
This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks.
NDP: Next Distribution Prediction as a More Broad Target
In our work, we extend the critique of NTP, highlighting its limitation also due to training with a narrow objective: the prediction of a sub-optimal one-hot distribution.
Augmenting Large Language Model Translators via Translation Memories
Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models.
The NiuTrans System for WNGT 2020 Efficiency Task
This paper describes the submissions of the NiuTrans Team to the WNGT 2020 Efficiency Shared Task.
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking
This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem.
Learning Architectures from an Extended Search Space for Language Modeling
Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell.
Multi-layer Representation Fusion for Neural Machine Translation
In this paper, we propose a multi-layer representation fusion (MLRF) approach to fusing stacked layers.
The NiuTrans Machine Translation Systems for WMT19
We participated in 13 translation directions, including 11 supervised tasks, namely EN↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔CS sub-track.
Sharing Attention Weights for Fast Transformer
This is even 16 times faster than the baseline with no use of the attention cache.
The NiuTrans Machine Translation System for WMT18
This paper describes the submission of the NiuTrans neural machine translation system for the WMT 2018 Chinese ↔ English news translation tasks.
A Simple and Effective Approach to Coverage-Aware Neural Machine Translation
We offer a simple and effective method to seek a better balance between model confidence and length preference for Neural Machine Translation (NMT).
