Data augmentation (DA) is a ubiquitous approach for several text generation tasks. Intuitively, in the machine translation paradigm, especially in low-resource languages scenario, many DA methods have appeared. The most commonly used methods are building pseudocorpus by randomly sampling, omitting, or...
摘要: Data augmentation is an approach for several text generation tasks. Generally, in the machine translation paradigm, mainly in low-resource language scenarios, many data augmentation methods have be...关键词: Artificial intelligence natural language processing neural network machine translation low-...
Data augmentationis a technique to increase the number of labelled examples required for DL training. It artificially enlarges the original training dataset by introducing various transformations such as translation, rotation, scaling, and even noise, to the original data instances, to make new instanc...
This is the source code of our method proposed in paper "DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks" accepted by EMNLP 2020. Examples flair_seq_tagger: sequense tagging model cd flair_seq_tagger; python train_tagger.py \ --data_dir PATH/TO/TRAIN_DIR ...
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages; Gözde Gül Şahin, Mark Steedman; Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We present...
Data augmentation for low resource languages INTERSPEECH 2014: 15th annual conference of the international speech communication association, International Speech Communication Association (ISCA) (2014), pp. 810-814 CrossrefView in ScopusGoogle Scholar Ranjan et al., 2017 Ranjan R., Castillo C.D., Ch...
1A Survey on Data Synthesis and Augmentation for Large Language ModelsKe WangonecallHangzhou Innovation Institute,Beihan
However, developing dialogue systems requires a large amount of training data, which is a challenge in low-resource domains and languages. Traditional data collection methods like crowd-sourcing are labor-intensive and time-consuming, making them ineffective in this context. Data augmentation (DA) is...
Prompt-based Data Augmentation for Low-Resource NLU Tasks This repository is the official implementation of PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks. Requirements To install requirements: conda create --name exp --file requirements.txt Pre-training Soft Prompt To obtain C4...
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages; Gözde Gül Şahin, Mark Steedman; Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We present...