This is the final project of Group 10 for the course Natural Language Understanding and
Computational Semantics Spring 2022.
In this project we aim to improve upon Patter-Exploiting Training (PET) by introducing
data augmentation techniques as well as multi-tasks tranining
There are 3 main components: PET, AdaPET and Data Augmentation, each with their individual README file from the original authors of the paper, viewers can follow the instruction to get the example result
We use EDA to generate new training examples from original dataset before feeding in to Aug-PET model
| Examples | Method | AG's | Yahoo Answer |
|---|---|---|---|
| τ = 10 | PET | 86.7% | 63.6% |
| τ = 10 | Aug-PET | 88.5% | 64.4% |
| τ = 50 | PET | 86.6% | 65.3% |
| τ = 50 | Aug-PET | 86.6% | 65.9% |
| τ = 100 | PET | 88.1% | 68.3% |
| τ = 100 | Aug-PET | 89.0% | 69.1% |
We present the results in ascending order of size of labelled data
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent