Text Generation

The final model was built on Flask and deployed to Heroku.

Scientific Text Generator

Objective

The goal of this project is to generate scientific sentences, when a scientific phrase or sentence is given.

Background

Text generation is an application of language modeling, and a subfield of natural language processing. It utilizes techniques in artificial intelligence to automatically generate natural language text, which fits in a certain communication context.

Text generation can be used to write stories, poems, emails, news articles, and more. It is also useful for machine translation and chatbots.

Descriptions

Note: Due to the lack of computing power, only the abstracts of the articles are selected to form the corpus.

flask-app

This folder contains all files for deployment to Heroku with Flask.

scraper.py

This file is used to collect papers information from NIPS website using scraping techniques.

text_preprocessing.jpynb

It is used for data cleaning, and save clean text into .txt file for model training.

Steps involved in text preprocessing:

• Words in British English are converted into American English.

Note: Although included in the data cleaning pipeline to reduce the vocabulary size , the following steps can be skipped for large corpus and with enough computing power.

• URLs, equations, citations are removed.
• Punctuation (except periods and commas), special characters are removed.
• Hyphenated descriptions like “video-related” are converted into separate words, like “video related”.
• All numbers are replaced with “NUMBER”.
• All characters are converted to lowercase.

text_generation_word_train.ipynb

It is used for data preparation, and data modeling. It outputs a language model for text generation.

Bidirectional LSTM is used to improve the performance.

A Python generator is used to save memory space, and solve scaling problem.

text_generation_word_generate.ipynb

It loads a pre-trained model created from text_generation_word_train.ipynb to generate text. A seed of a text sequence needs to be provided so that the model can generate text from there.

gpt2_train.ipynb and gpt2_generate.jpynb

GPT-2 model is used to evaluate the performance of the developed LSTM model.

gpt2_train.jpynb takes the cleaned text file from text_preprocessing.jpynb, and uses GPT-2 to retrain a model. Then it passes the trained model to gpt2_generate.jpynb to generate text.

For more details regarding how to use a GPT-2 model, please refer to gpt-2-simple

Future work

Several ways that can further improve the model:

• In text preprocessing part, add spell checker
• Use the entire articles to form the corpus
• Do rigorous parameters tuning
• Add more LSTM layers
• Try seq2seq model

References:

[1] Andrej Karpathy, 2015, The Unreasonable Effectiveness of Recurrent Neural Networks

[2] Schuster, Mike, and Kuldip K. Paliwal, 1997, Bidirectional recurrent neural networks

[3]Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever, 2019, Language Models are Unsupervised Multitask Learners