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We evaluate the dialogue generation models on their performance on probe tasks as discussed in our paper. The codebase shares the dataset used in the paper and the codebase that can be used to recreate the results in the paper. We hope the codebase serves as a base to work on further improvements on the results of the paper.

The modified dataset containing the probe tasks and the script to generate the probe tasks can be found in /dataset

Package Requirements

We use python 3.6 for all the experiments in the paper.

pip install -r requirements.txt

Training the model

Use the --model argument to train the appropriate model. The options include hred, seq2seq, seq2seq_attn, transformer and bilstm_attn

python --model bilstm_attn --dataset MultiWoZ --batch_size 32 --s2s_hidden_size 256 --s2s_embedding_size 128

Evaluating on the ProbeTasks

The probe tasks are evaluated in The choice of selecting the probetasks can be done by selecting the appropriate column index in probe tasks. currently evaluates models on the probetasks sequetially.


The command writes the results on to a csv that can be used to plot the graphs in the appendix. The tables can be obtained from the CSV.

Plotting the results

To generate the graphs use the following command from utils/:

python --dataset MultiWoZ

Also you can use utils/ to generate latex table with error range.

python --dataset MultiWoZ

Alternate Metrics

To select models with alternate metrics run utils/ The code will return a csv with the epoch number for the best model with METEOR, ROUGE-F1, BERT (Average) and BLEU.

python --dataset MultiWoZ

To precompute the BERT embeddings for the vocabulary, use python --dataset MultiWoZ.


If you find this work useful and use it in your own research, please consider citing our paper.

  author       = {Parthasarathi, Prasanna and Chandar, Sarath and Pineau, Joelle},
  title        = {Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?},
  year         = {2021},
  booktitle    = {Proceedings of the 22nd Annual SIGdial Meeting on Discourse and Dialogue},
  publisher    = {Association for Computational Linguistics},


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