DeepAI AI Chat
Log In Sign Up

Multilingual Augmenter: The Model Chooses

02/19/2021
by   Matthew Ciolino, et al.
0

Natural Language Processing (NLP) relies heavily on training data. Transformers, as they have gotten bigger, have required massive amounts of training data. To satisfy this requirement, text augmentation should be looked at as a way to expand your current dataset and to generalize your models. One text augmentation we will look at is translation augmentation. We take an English sentence and translate it to another language before translating it back to English. In this paper, we look at the effect of 108 different language back translations on various metrics and text embeddings.

READ FULL TEXT

page 11

page 12

page 13

07/04/2020

Text Data Augmentation: Towards better detection of spear-phishing emails

Text data augmentation, i.e. the creation of synthetic textual data from...
01/09/2022

An Ensemble Approach to Acronym Extraction using Transformers

Acronyms are abbreviated units of a phrase constructed by using initial ...
02/24/2023

STA: Self-controlled Text Augmentation for Improving Text Classifications

Despite recent advancements in Machine Learning, many tasks still involv...
09/14/2022

vec2text with Round-Trip Translations

We investigate models that can generate arbitrary natural language text ...
07/06/2019

Evolutionary Algorithm for Sinhala to English Translation

Machine Translation (MT) is an area in natural language processing, whic...