WARNING: WORK IN PROGRESS

This repository is not ready yet. Please don't clone or use right now. A functioning prototype should be ready in the next weeks.

Attention-based Chatbot for Twitter Customer Support.

This is a Chatbot for Twitter Customer Support. It is implemented as a Seq2seq RNN with Multiplicative Attention mechanism.

Data have been taken from Kaggle's Customer Support on Twitter dataset. This dataset comprehends tweet exchanges from multiple companies. Each company requires a different model implementation.

How it works

The model implemented is a Seq2Seq Neural Network with LSTM layers and Luong's Multiplicative Attention. It is written in TensorFlow 2.1 and optimized with Autograph. The model is character-based, i.e. single characters are tokenized and predicted. Training is implemented with teacher forcing.

Structure of the Repository

Folders:

• /data_raw: uncompressed raw dataset must be pasted here.
• /data_processed: all pre-processed observations will be saved in /Training, /Validation and /Test sub-folders. It contains also .yaml dictionaries to translate from token (character) to vector; their naming convention is char2idx_{company}.yaml.
• /dataprep: contains all data preprocessing scripts, with names as dataprep_{company}.py.
• /tools: useful functions to be iterated in dataprep. One main tools.py module contains functions used for all models. For more company-specific tools other modules are available as tools_{company}.py.
• /saved_models: where trained models are saved and/or loaded after launching train.py.
• /talk: contains a list of scripts to be called from terminal to chat with a trained model. Naming convention is still talk_{company}.py.

Files:

• config.yaml: main configuration file. Every hyperparameter and model choice can be decided here.
• model.py: model implementation.
• train.py: model training. The company's model and the data to be trained on can be chosed from config.yaml.

Modules

langdetect==1.0.8
tensorflow==2.1.0
numpy==1.18.1
pandas==1.0.1

Bibliography

Papers:

• Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. "Effective approaches to attention-based neural machine translation." arXiv preprint arXiv:1508.04025 (2015).

Other useful resources:

• The official TensorFlow tutorial on a Neural machine translation with attention.
• Attention-based Sequence-to-Sequence in Keras on Wanasit Tanakitrungruang's blog.