frankxu2004 / AI-writer_Data2Doc

PyTorch Implementation of NBA game summary generator.

AI writter - Data2Doc

This is the project for automatically generating summarizatons given NBA game box-score.

Requirements

1. Python 3.6
2. PyTorch 0.2

Data set

We use Rotowire dataset for training as in Challenges in Data-to-Document Generation (Wiseman, Shieber, Rush; EMNLP 2017). This dataset consists of (human-written) NBA basketball game summaries aligned with their corresponding box- and line-scores.

Basic Usage

1. First extract the dataset, using tar -jxvf boxscore/rotowire.tar.bz2.
2. Go to directory train/, using cd train
3. (Optional) Train the model, using python3 train.py.
4. Generate some text, using python3 small_evaluate.py

Some pre-trained model files could be found here. Extract them under model/ directory.

• Sample Generate Summary using BiLSTM encoder

The Chicago Bulls defeated the Orlando Magic , 102 - 92 , at
United Center on Saturday . The Bulls ( 10 - 2 ) were expected
to win this game rather easily and they did n’t disappoint in the
fourth quarter . In fact , they outscored the Magic by 12 points in
the fourth quarter to pull out the win . Defense was key for the
Bulls , as they held the Magic to 37 percent from the field and 25
percent from three - point range . The Magic also dominated the
rebounding , winning that battle , 44 - 32 . The Magic ( 8 - 18 )
had to play this game without Joel Embiid and Nikola Vucevic ,
as they simply did n’t have enough to get out of hand . Jimmy
Butler was the player of the game , as he tallied 23 points , five
rebounds and four assists . Aaron Gordon was the only other
starter with more than 10 points , as he totaled 15 points , five
rebounds and four assists. . . ...

Settings

In train/settings.py you can find the configurations.

Parameter for model

• LAYER_DEPTH = 2, the depth of recurrent unit, default = 2.
• EMBEDDING_SIZE = 600, the hidden size for embedding, default = 600.

Parameter for training

• MAX_SENTENCES = None, limit the maximum length for training. Set lower (e.g 5) for faster training speed. Set None to train for entire corpus.
• LR = 0.01, learning rate.
• ITER_TIME = 10000
• BATCH_SIZE = 8
• USE_MODEL = None, load trained model before training.

Parameter for display

• GET_LOSS = 1, print out average loss every GET_LOSS iterations.
• SAVE_MODEL = 5000, save the model every SAVE_MODEL iterations.
• OUTPUT_FILE = 'default', starting name for saving the model. During training, encoder and decoder would be saved as [OUTPUT_FILE]_[encoder|decoder]_[iter_time] every SAVE_MODEL iteratons.
• ENCODER_STYLE = 'BiLSTM', choose encoder model, currently I have 3 styles -- 'RNN', 'LIN' and 'BiLSTM'. All these archotectures are within seq2seq with attention framework.

More Details

Train

In train/ directory is the part of data2text generation. The files are for this part include:

• dataprepare.py -- word2index map class, storing the vocabulary and relations
• model.py -- The file that contains the implementation of several encoder, decoder and embedding model class
• preprocessing.py -- mainly for read of parse the data
• train.py -- the file that defines the training processes
• util.py -- utility functions for time, showing etc.
• setting.py -- store the hyper-parameter, file location etc.

Evaulate

In evaluate/ directory is the extraction evaluation system, based on Challenges in Data-to-Document Generation (Wiseman, Shieber, Rush; EMNLP 2017) and part of their codes. The files from their repo contains:

• data_utils.py -- for the data parsing and cleaning
• extractor.lua -- the main script for relation classifier
• non_rg_metrics.py -- CS and RO computation

Thanks to the dataset and code from Wiseman et. al.