Transformer-Based Conditioned Variational Autoencoder for Dialogue Generation
In human dialogue, a single query may elicit numerous appropriate responses. The Transformer-based dialogue model produces frequently occurring sentences in the corpus since it is a one-to-one mapping function. CVAE is a technique for reducing generic replies. In this paper, we create a new dialogue model (CVAE-T) based on the Transformer with CVAE structure. We use a pre-trained MLM model to rewrite some key n-grams in responses to obtain a series of negative examples, and introduce a regularization term during training to explicitly guide the latent variable in learning the semantic differences between each pair of positive and negative examples. Experiments suggest that the method we design is capable of producing more informative replies.
Code (0)
등록된 구현이 없습니다.
Tasks
Dialogue GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Variational Transformers for Diverse Response Generation
Despite the great promise of Transformers in many sequence modeling tasks (e.g., machine translation), their deterministic nature hinders them from generalizing to high entropy tasks such as dialogue response generation.…
DecoderDiversityMachine TranslationResponse Generation+1Conditioned Query Generation for Task-Oriented Dialogue Systems
Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative solution is to rely on automatic text gen…
DiversityTask-Oriented Dialogue SystemsText GenerationT-CVAE: Transformer-Based Conditioned Variational Autoencoder for Story Completion
Story completion is a very challenging task of generating the missing plot for an incomplete story, which requires not only understanding but also inference of the given contextual clues. In this paper, we present a nove…
DecoderDiversityStory CompletionPrompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue
Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems. To address the catastrophic forgetting issue of LL, generative replay methods are widely employed to consolidate past knowledge with gene…
Lifelong learningNatural Language UnderstandingConditioned Text Generation with Transfer for Closed-Domain Dialogue Systems
Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative solution is to rely on automatic text gen…
Data AugmentationDiversityLanguage ModellingTask-Oriented Dialogue Systems+1