Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation
Prakhar Gupta, Harsh Jhamtani, Jeffrey P. Bigham · Findings of the Association for Computational Linguistics: NAACL 2022 · 2022
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 nonobtrusive 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. 1 Context: i like the sand on my feet Target: my puppy is called georgie. GPT-2: My mom likes the water.Multigen: My pet is the gecko.CODA: My dog walks along the beach with sand.CODA-Path: sand is at location beach belongs to walk