ammieqi / hdsa-dialog

This is the code for ACL 2019 "Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention"

HDSA-Dialog

This is the code and data for ACL 2019 long paper "Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention". The up-to-date version is in http://arxiv.org/abs/1905.12866.

The full architecture is displayed as below:

The architecture consists of two components:

• Dialog act predictor (Fine-tuned BERT model)
• Response generator (Hierarchical Disentangled Self-Attention Network)

The basic idea of the paper is to do enable controlled reponse generation under the Transformer framework, where we construct a dialog act graph to represent the semantic space in MultiWOZ tasks. Then we particularly specify different heads in different levels to a specific node in the dialog act graph. For example, the picture above demonstrates the merge of two dialog acts "hotel->inform->location" and "hotel->inform->name". The generated sentence is controlled to deliever message about the name and location of a recommended hotel.

Requirements

• Python 3.5
• Pytorch 1.0
• Pytorch-pretrained-BERT

Please see the instructions to install the required packages before running experiments.

Dialog Act Predictor

This module is used to predict the next-step dialog acts based on the conversation history. Here we adopt the state-of-the-art NLU module BERT to get the best prediction accuracy.

Reproducibility

• We release the pre-trained predictor model in google drive, you can put the zip file into checkpoints/predictor and unzip it to get the save_step_15120 folder.
• We already put the pre-trained generator model under checkpoints/generator, you can use this model to obtain 23.6 BLEU on the delexicalized test set.