An implementation of the paper Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models and Mutual Information and Diverse Decoding Improve Neural Machine Translation

Results

The model is able to replicate the results of the paper.

Model 1 python3.6 main.py -n full_final2 -tc -bms 20 -bs 100 -e 80 -seshid 300 -uthid 300 -drp 0.4 -lr 0.0005

Model 2 (curriculum learning with inverse sigmoid teacher forcing ratio decay)

python3.6 main.py -n curlrn -bi -lm -nl 2 -lr 0.0003 -e 10 -seshid 300 -uthid 300

Model 3 (100% teacher forcing) python3.6 main.py -n onlytc -nl 2 -bi -lm -drp 0.4 -e 25 -seshid 300 -uthid 300 -lr 0.0001 -bs 100 -tc

• We notice over fitting on the validation loss (patience 3) from epoch 8 onwards for the first, second model and from epoch 24 (smaller learning rate) for the 1st one
• Training time is about 15 mins(30 mins w/o teacher forcing) per epoch on a Tesla Geforce GTX Titan X consuming about 11GB of GPU RAM
• Beam search decoding with size 50 is used, MMI anti-LM is used for ranking the results
• Test set results (w/ ground truth for teacher forcing 100%) available here
• Test set results (w/ ground truth for curriculum learning) available here
• For inference use the flags -test -mmi -bms 50

Notes

• Greedy decoding is used during training time if teacher forcing is disabled (by default we train with tc)
• During inference time the MMI-antiLM score is computed as per equation (15) in Jiwei Li (Diversity Promoting Paper)
• an LM loss is by default included (through an additional plain RNN) and this jointly trained with the other parameters, although not much difference in results are obtained and to speedup I often disable it
• When processing the data, diverse sequence lengths in a given batch leads to better optimization so no sorting of training data
• Validation/test perplexity is calculated using teacher forcing as we want to capture the true work log likelihood
• Inference or generation is using beam search
• Note with curriculum learning we get more diversity during generation or inference time (almost

Train

python3.6 main.py -tc -e 100 -n full_tc -bms 20 -bs 80

A brief list of options is given below, for a longer list please see main.py file

• tc says that use teacher forcing for the entire training procedure
• bms is the beam size for decoing used only during inference time, during training if teacher forcing is disabled greedy decoding is used
• n is a required parameter that gives a name to the model files
• bs is the batch size
• e is the number of epochs
• test boolean switch says to run only inference mode
• btstrp flag gives a name of a pre-trained model which is used for parameter initializations instead of the default of gaussian mean 0 and standard deviationn 0.01

Sanity check:-

• If you load a small training set, like 1000 training and 100 valid as here train_dataset, valid_dataset = MovieTriples('train', 1000), MovieTriples('valid', 100) and train to overfit the model converges to 0.5 training loss in 50 epochs with training command python3.6 main.py -n sample -tc -bms 20 -bs 100 -e 50 -seshid 300 -uthid 300 -drp 0.4

• Some samples generated at inference as compared to ground truth (on the test set) are

  [("i don ' t know . ", -11.935150146484375), ("  i ' m in from new york . i came to see <person> .  ", -20.482309341430664), ("  i ' ll take you there , sir .  ", -16.400659561157227), ("  i ' m sorry , but no one by that name lives here .  ", -22.178613662719727), ("  i know it ' s none of my business --  ", -18.43322467803955), ("  i don ' t think you win elections by telling <number> percent of the people that they are .  ", -27.444936752319336), ("  you ' re going to break up with <person> , aren ' t you ?  ", -23.688961029052734), ("  i ' m afraid not .  ", -14.662097930908203), ("  i don ' t know , do you ? <continued_utterance> it ' s a <person> .  ", -25.888113021850586), ("  i ' ll be right back .  ", -15.958183288574219)]Ground truth [("  what ' s bugging her ?  ", 0)]