Despite the recent advent of dialog systems, still many challenges are unresolved and the system often generates responses causing a breakdown in the interaction between a user and the system. The dialog breakdown significantly damages user experience, and thus detecting such failure is significant. We propose a model to detect dialog breakdown using the recurrent neural network and attention layer to embed a previous dialog context. This model determines the probability of breakdown using the extracted dialog context vector and the target sentence's representation vector. We submitted this study to the Dialog Breakdown Detection Challenge 3 of Dialog System Technology Challenge 6, and the results showed that it significantly outperforms the most of other models in estimating breakdown probability.
You can find more detailes in our workshop paper.
Recommend you to use virtualenv.
- Install pytorch
pip3 install cffipip3 install -r requirements.txt
- you need data files in data directory
- pre-trained glove twitter vectors
- DBDC 3 dataset Note that you need to modify the given data into the proper format for our model Automatic conversion code will be provided soon.
- check the parser options
python3 main.py
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Copyright 2017 NAVER Corp.
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