borisanimal / pmldl

Modern Application Production

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Language: Python

Version Control: GitHub

Tests: Python unittest package

Tasks: Trello

User Stories: GitHub issues

Project Proposal

General

These days jokes and memes become part of modern people's life. Each day everybody spends time consuming content. Communities like Reddit or 9GAG periodically release meme calendars.

We propose a question-answer joke generator telegram bot with the aim to distinguish if artificially generated content can entertain users at the same level as human-created jokes.

Training

For training, we will apply transfer learning. As a starting model we will try different state-of-the-art language models, such as BERT [2], GPT-2 [3]. As a training dataset will use set from Kaggle [1], which we will extend with other 2-liner jokes from the internet.

Evaluation

Joking is a subjective topic, so we are planning to ask people to evaluate samples of generated jokes. And afterward, we'll compare the results for different models.

Final result

We are planning to deploy this project into telegram infrastructure for simple interaction with the user. The flexibility of this platform covers all requirements described above such as: deliver content, collecting evaluation from users and easy deployment.

1. Kaggle dataset
2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
3. Language Models are Unsupervised Multitask Learners. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever