Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators

by   Sanghyun Yi, et al.

Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood(MLE) objective they suffer from issues such as lack of generalizability and the generic response problem, i.e., a system response that can be an answer to a large number of user utterances, e.g., "Maybe, I don't know." Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches. Towards this goal, we present a system that evaluates chatbot responses at each dialog turn for coherence and engagement. Our system provides explicit turn-level dialog quality feedback, which we show to be highly correlated with human evaluation. To show that incorporating this feedback in the neural response generation models improves dialog quality, we present two different and complementary mechanisms to incorporate explicit feedback into a neural response generation model: reranking and direct modification of the loss function during training. Our studies show that a response generation model that incorporates these combined feedback mechanisms produce more engaging and coherent responses in an open-domain spoken dialog setting, significantly improving the response quality using both automatic and human evaluation.


page 1

page 2

page 3

page 4


Improving Neural Conversational Models with Entropy-Based Data Filtering

Current neural-network based conversational models lack diversity and ge...

Measuring the `I don't know' Problem through the Lens of Gricean Quantity

We consider the intrinsic evaluation of neural generative dialog models ...

Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

We present a novel training framework for neural sequence models, partic...

Dialogue Response Ranking Training with Large-Scale Human Feedback Data

Existing open-domain dialog models are generally trained to minimize the...

Atom Responding Machine for Dialog Generation

Recently, improving the relevance and diversity of dialogue system has a...

Neuroscientific User Models: The Source of Uncertain User Feedback and Potentials for Improving Web Personalisation

In this paper we consider the neuroscientific theory of the Bayesian bra...

Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network

Neural conversational models learn to generate responses by taking into ...

Please sign up or login with your details

Forgot password? Click here to reset