The objective of this project is to explore neural network architectures for question answering and goal oriented dialog systems. We will train on (6) dialog bAbI tasks. Tensorflow is used for building the models. I have listed some of the influential/interesting papers below. If I have missed something, let me know.
Documentation is available as wiki.
This section presents the set of 6 tasks for testing end-to-end dialog systems in the restaurant domain. Each task tests a unique aspect of dialog.
# python2 is not supported
sudo -H pip3 install -r requirements.txt
# if this doesn't work, raise an issue# run main.py without arguments, for usage information
# usage: main.py [-h] (-i | -t) [--task_id TASK_ID] [--batch_size BATCH_SIZE]
# [--epochs EPOCHS] [--eval_interval EVAL_INTERVAL]
# [--log_file LOG_FILE]
# main.py: error: one of the arguments -i/--infer -t/--train is required
python3 main.py --train --task_id=3 --log_file=log.task3.txt| Task | Training Accuracy | Validation Accuracy |
|---|---|---|
| 1 | 100 | 99.7 |
| 2 | 100 | 100 |
| 3 | 100 | 74.71 |
| 4 | 100 | 56.67 |
| 5 | 100 | 98.42 |
| 6 | 76.61 | 47.08 |
| Task | Training exmaples | Accuracy on Dev set |
|---|---|---|
| 5 | 200 | 100% |
- Learning End-to-End Goal-Oriented Dialog, review
- A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue
- Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
- Gated End-to-End Memory Networks
- Query-Reduction Networks for Question Answering
- Ask Me Even More: Dynamic Memory Tensor Networks
- OOV : Out Of Vocabulary
- hops : number of memory lookups
- Tweaked vyraun's chatbot-MemN2N-tensorflow repository, for using Memory Networks for bAbI dialog tasks