Sampling and Filtering of Neural Machine Translation Distillation Data

04/01/2021
by   Vilém Zouhar, et al.
0

In most of neural machine translation distillation or stealing scenarios, the goal is to preserve the performance of the target model (teacher). The highest-scoring hypothesis of the teacher model is commonly used to train a new model (student). If reference translations are also available, then better hypotheses (with respect to the references) can be upsampled and poor hypotheses either removed or undersampled. This paper explores the importance sampling method landscape (pruning, hypothesis upsampling and undersampling, deduplication and their combination) with English to Czech and English to German MT models using standard MT evaluation metrics. We show that careful upsampling and combination with the original data leads to better performance when compared to training only on the original or synthesized data or their direct combination.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

02/06/2017

Ensemble Distillation for Neural Machine Translation

Knowledge distillation describes a method for training a student network...
12/16/2021

Isometric MT: Neural Machine Translation for Automatic Dubbing

Automatic dubbing (AD) is among the use cases where translations should ...
12/31/2020

Exploring Monolingual Data for Neural Machine Translation with Knowledge Distillation

We explore two types of monolingual data that can be included in knowled...
06/12/2021

Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation

Although teacher forcing has become the main training paradigm for neura...
10/12/2020

Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation

Scarcity of parallel sentence-pairs poses a significant hurdle for train...
12/03/2018

Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling

Knowledge distillation is an effective technique that transfers knowledg...
07/14/2020

Modeling Voting for System Combination in Machine Translation

System combination is an important technique for combining the hypothese...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.