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mxnet-seq2seq

This project implements the sequence to sequence learning with mxnet for open-domain chatbot

Sequence to Sequence learning with LSTM encoder-decoder

The seq2seq encoder-decoder architecture is introduced by Sequence to Sequence Learning with Neural Networks

This implementation borrows idea from lstm_bucketing, I slightly modified it and reconstructed the embedding layer.

How to run

Firstly, process the data by

python datautils.py

then run the model by

python main.py

The architecture

We know that seq2seq encoder-decoder architecture includes two RNNs (LSTMs), one for encoding source sequence and another for decoding target sequence.

For NLP-related tasks, the sequence could be a natural language sentence. As a result, the encoder and decoder should share the word embedding layer .

The bucketing is a grate solution adapting the arbitrariness of sequence length. I padding zero to a fixed length at the encoding sequence and make buckets at the decoding phrase.

The data is formatted as:

0 0 ... 0 23 12 121 832 || 2 3432 898 7 323
0 0 ... 0 43 98 233 323 || 7 4423 833 1 232
0 0 ... 0 32 44 133 555 || 2 4534 545 6 767
---
0 0 ... 0 23 12 121 832 || 2 3432 898 7
0 0 ... 0 23 12 121 832 || 2 3432 898 7
0 0 ... 0 23 12 121 832 || 2 3432 898 7
---

The input shape for embedding layer is (batch_size, seq_len), the input shape for lstm encoder is (batch_size, seq_len, embed_dim) .

More details coming soon

For any question, please send me email.

yoosan.zhou at gmail dot com