TableQA

Question answering on tables using deep learning.

Prerequisites

• keras
• numpy

Methods

• RNN

RNN / Embed / Sent / Query = <class 'keras.layers.recurrent.LSTM'>, 50, 100, 100

• MemNN (LSTM)

Keras implementation

Dataset

consists of samples of T/QA (table/question+answer)

1. Syntheticaly generated

Patterns:

• Pattern1

1 City : Graz, Immigration : 13 . 2 City : Wien, Immigration : 18 . 3 What is the immigration in Graz? 13 1

Vocab size: 31 unique words Story max length: 24 words Query max length: 7 words

Number of training samples: 10000 Train on 9500 samples, validate on 500 samples Number of test samples: 500

inputs_train shape: (10000, 24) inputs_test shape: (500, 24)

queries_train shape: (10000, 7) queries_test shape: (500, 7)

answers_train shape: (10000, 31) answers_test shape: (500, 31)

• Pattern2

1 City : Bludenz, Immigration : 11 , Emmigration : 18. 2 City : Feldkirch, Immigration : 12, Emmigration : 17 . 3 What is the Emmigration in Feldkirch? 17 2

Vocab size: 30 unique words Story max length: 24 words Query max length: 7 words Number of training samples: 10000 Train on 9500 samples, validate on 500 samples Number of test samples: 500

2. Real table data (for training and testing)

Vocab size: 1944 unique words Story max length: 320 words Query max length: 13 words Number of training samples: 2330 Number of test samples: 16

3. Simulated table data

Vocab size: 43 unique words Story max length: 400 words Query max length: 7 words Number of training samples: 12078 Number of test samples: 557

??? 4. Simulated table data + Real table data (simulated data based on the real table data for training and real table data for testing only)

• Training 9002 (8551 + 451 validation set)
• Test ?

Results

1. Synthetic patterns

• Pattern1

** RNN

Training loss: 0.0181 - acc: 0.9953 - val_loss: 0.0467 - val_acc: 0.9860

Test loss / test accuracy = 0.0245 / 0.9900

** MemNN

=> 120 Epochs

Training loss: 0.1300 - acc: 0.9584 - val_loss: 0.0294 - val_acc: 1.0000

Test loss / test accuracy = 7.9514 / 0.2000

[Finished in 401.3s]

=> 120 Epochs + earlyStopping (patience=2)

/ Epoch 39

Training loss: 0.8698 - acc: 0.5498 - val_loss: 0.7459 - val_acc: 0.5200

Test loss / test accuracy = 3.5823 / 0.2680

[Finished in 146.1s]

• Pattern2

** RNN

=> 40 Epochs

Training loss: 0.0359 - acc: 0.9928 - val_loss: 3.8414e-04 - val_acc: 1.0000

Test loss / test accuracy = 0.0004 / 1.0000

[Finished in 384.9s]

=> 40 Epochs + earlyStopping (patience=2)

** MemNN

2. Real table data (training and testing on the samples of the original sample table)

Training loss: 5.6899 - acc: 0.0190 - val_loss: 6.5770 - val_acc: 0.0256

Test loss / test accuracy = 7.3377 / 0.0000

3. Simulated table data + Real table data (generating training data based on the sample table statistics to reduce vocabulary varience: max 10 unique values per column. Testing on the original table.)

!!!

4. Simulated table data

** RNN

=> 40 Epochs

Training loss: 1.5180 - acc: 0.3019 - val_loss: 1.4857 - val_acc: 0.3278 Test loss / test accuracy = 1.5054 / 0.2855

[Finished in 8814.4s]

License

This project is licensed under the MIT License

Acknowledgments

• Implementation based on Keras babi_rnn

References

Keras

• Deep Language Modeling for Question Answering using Keras. Benjamin Bolte. 2016

Tables

• Neural Enquirer: Learning to Query Tables with Natural Language. IJCAI. 2016