/hred-tensorflow

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HRED (TensorFlow)

HRED is a basic model for multiple turn dialog. Here, we implement HRED with attention mechanism. You can refer to the following papers for details:

Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., & Pineau, J. (2016, February). Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models. In AAAI (Vol. 16, pp. 3776-3784).

Require Packages

  • cotk
  • TensorFlow == 1.13.1
  • TensorBoardX >= 1.4

Quick Start

  • Downloading dataset and save it to ./data. (Dataset will be released soon.)
  • Execute python run.py to train the model.
    • The default dataset is UbuntuCorpus. You can use --dataset to specify other dataloader class.
    • It don't use pretrained word vector by default setting. You can use --wvclass to specify wordvector class. For example: --wvclass gloves
    • If you don't have GPUs, you can add --cpu for switching to CPU, but it may cost very long time.
  • You can view training process by tensorboard, the log is at ./tensorboard.
    • For example, tensorboard --logdir=./tensorboard. (You have to install tensorboard first.)
  • After training, execute python run.py --mode test --restore best for test.
    • You can use --restore filename to specify checkpoints files, which are in ./model.
    • --restore last means last checkpoint, --restore best means best checkpoints on dev.
  • Find results at ./output.

Arguments

    usage: run.py [-h] [--name NAME] [--restore RESTORE] [--mode MODE]
                  [--dataset DATASET] [--datapath DATAPATH] [--epoch EPOCH]
                  [--wvclass WVCLASS] [--wvpath WVPATH] [--out_dir OUT_DIR]
                  [--log_dir LOG_DIR] [--model_dir MODEL_DIR]
                  [--cache_dir CACHE_DIR] [--cpu] [--debug] [--cache]
    
    optional arguments:
      -h, --help            show this help message and exit
      
    useful arguments:
      --name NAME           The name of your model, used for variable scope and 
                            tensorboard, etc.
                            Default: runXXXXXX_XXXXXX (initialized by current time)
      --restore RESTORE     Checkpoints name to load. "last" for last checkpoints,
                            "best" for best checkpoints on dev. Attention: "last"
                            and "best" wiil cause unexpected behaviour when run 2
                            models in the same dir at the same time. Default: None
                            (dont load anything)
      --mode MODE           "train" or "test". Default: train
      --dataset DATASET     Dataloader class. Default: OpenSubtitles
      --datapath DATAPATH   Directory for data set. Default: ./data
      --epoch EPOCH         Epoch for trainning. Default: 100
      --wvclass WVCLASS     Wordvector class, none for not using pretrained
                            wordvec. Default: None
      --wvpath WVPATH       Directory for pretrained wordvector. Default:
                            ./wordvec
    
    advanced arguments:
      --out_dir OUT_DIR     Output directory for test output. Default: ./output
      --log_dir LOG_DIR     Log directory for tensorboard. Default: ./tensorboard
      --model_dir MODEL_DIR
                            Checkpoints directory for model. Default: ./model
      --cache_dir CACHE_DIR
                            Checkpoints directory for cache. Default: ./cache
      --cpu                 Use cpu.
      --debug               Enter debug mode (using ptvsd).
      --cache               Use cache for speeding up load data and wordvec. (It
                       	    may cause problems when you switch dataset.)

TensorBoard Example

Execute tensorboard --logdir=./tensorboard, you will see the plot in tensorboard pages:

hred_plot_example

Following plot are shown in this model:

  • train/loss

  • train/perplexity

  • dev/loss

  • dev/perplexity

  • test/loss

  • test/perplexity

And text output:

hred_text_example

Following text are shown in this model:

  • args

Case Study of Model Results

Execute python run.py --mode test --restore best

The following output will be in ./output/[name]_[dev|test].txt:

perplexity:     74.407300
bleu:   0.089069

post:   anyone knows why my stock oneiric exports env var <unk> I mean what is that used for? I know of $USER but not <unk> . My precise install doesn't export USERNAME <eos>
resp:   looks like it used to be exported by <unk> but the line had the comment <unk> <unk> Is this <unk> so I guess it isn't surprising it is gone <eos>
gen:    you <eos>
post:   looks like it used to be exported by <unk> but the line had the comment <unk> <unk> Is this <unk> so I guess it isn't surprising it is gone <eos>
resp:   thanks! How the heck did you figure that out? <eos>
gen:    I have no idea what that is <eos>
post:   thanks! How the heck did you figure that out? <eos>
resp:   <unk> <eos>
gen:    I <eos>
post:   <unk> <eos>
resp:   nice thanks! <eos>
gen:    I <eos>

post:   i set up my hd such that i have to type a passphrase to access it at boot. how can i remove that <unk> and just boot up normal. i did this at install, it works fine, just tired of having reboots where i need to be at terminal
resp:   backup your data, and re-install without encryption "might" be the easiest method <eos>
gen:    you can install the ubuntu package <eos>
post:   backup your data, and re-install without encryption "might" be the easiest method <eos>
resp:   so you dont know, ok, anyone else? <eos> you are like, yah my mouse doesnt work, reinstall your os lolol what a joke <eos>
gen:    I have no idea what you mean by the <eos>

Performance

Perplexity BLEU
UbuntuCorpus 74.20 0.112

Author

Zhou Hao