One-Way Prototypical Networks

• An Implementation of One-Way Prototypical Networks with some modifications.
• The dataset is from Kyoto University Web Document Leads Corpus and we modified the dataset to binary classification (KWDLC-R).
• We set the "CONTINGENCY.Cause" relation sentence pairs as label yes and other discourse relation to no.

Features

• Utilizes the CL-tohoku BERT-based Japanese language model.
• Calculates embeddings for discourse relation sentences pairs.
• Computes the Prototypical Network's forward pass.
• Provides evaluation metrics such as precision, recall, and F1-score.
• Includes support for calculating and retrieving embeddings for support and query batches.

Model

• cl-tohoku/bert-base-japanese-v2

Requirements

pip install -r requirements.txt

Dataset

Data Split	Size
Train Data	2087
Validation Data	261
Test Data	262

Loss Function

• binary cross-entropy

Normal Distribution and Probability Calculation

In this implementation, a normal distribution is used to model the similarity between the mean support embedding and query embeddings. The probability of a data point belonging to the positive class is computed based on the distance between these embeddings.

Normal Distribution

A normal distribution, also known as a Gaussian distribution, is a probability distribution that is symmetric and bell-shaped. It is characterized by two parameters: the mean (μ) and the standard deviation (σ). In your code, a normal distribution with a fixed mean of 0.0 and a standard deviation of self.std is used.

The probability density function (PDF) of the normal distribution is defined as:

$$f(x) = \frac{1}{\sigma \sqrt{2\pi}} \cdot e^{-\frac{(x - \mu)^2}{2\sigma^2}}$$

Train

python oneway_protoNet.py

Test

python oneway_test.py

Embedding Visualization

python visualize_proto.py

Reference

• A. Kruspe, One-way prototypical networks. arXiv preprint arXiv:1906.00820, 2019.
• Snell, Jake, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. Advances in neural information processing systems, 2017.
• 岸本裕大, 村脇有吾, 河原大輔, 黒橋禎夫. 日本語談話関係解析：タスク設計・談話標識の自動認識・ コーパスアノテーション, 自然言語処理, Vol.27, No.4, pp.889-931, 2020.

Collaborators

• Kohei Oda - Github
• Heine Chu - Github

Contact

For inquiries, please don't hesitate to email [email protected]