Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation

Code for paper Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation- Link (NAACL 2022 Findings paper)

Overview

Target-guided response generation enables dialogue systems to smoothly transition a conversation from a dialogue context toward a target sentence. Such control is useful for designing dialogue systems that direct a conversation toward specific goals, such as creating non-obtrusive recommendations or introducing new topics in the conversation. In this paper, we introduce a new technique for target-guided response generation, which first finds a bridging path of commonsense knowledge concepts between the source and the target, and then uses the identified bridging path to generate transition responses. Additionally, we propose techniques to re-purpose existing dialogue datasets for target-guided generation. Experiments reveal that the proposed techniques outperform various baselines on this task. Finally, we observe that the existing automated metrics for this task correlate poorly with human judgement ratings. We propose a novel evaluation metric that we demonstrate is more reliable for target-guided response evaluation. Our work generally enables dialogue system designers to exercise more control over the conversations that their systems produce.

Data and model files

Please find data and trained models for this paper in this Google drive folder

Commonsense model

Scripts present in folder scripts/commonsense_models

Data augmentation

Scripts for data augmentation using Dailydiaog dataset present in folder scripts/data_augmentation

Targetcoherence metric

Scripts present in folder scripts/targetcoherence_metric

Path generation

Scripts present in folder scripts/path_generation

Response generation

Scripts present in folder scripts/response_generation