Retrieval Bias Aware Ensemble Model for Conditional Sentence Generation.

Conditional sentence generation aims to generate proper target sentences with the given condition, and has shown great promise in many text generation applications such as dialogue systems and poetry generation. The ensemble of retrieval and generation-based models retrieve texts according to the input condition to assist the generation-based model. Those approaches obtain great performance tasks as they can absorb both merits to generate informative and coherent sentences. However, the input condition and its retrieved results are usually not highly consistent due to the quality of retrieval. It leads to a retrieval bias between the condition and its retrieved result, and then text generation augmented by such results becomes unreliable. To fix this issue, we propose RBAEM, a Retrieval Bias Aware Ensemble Model. RBAEM employs two CVAEs (Conditional variational Auto-encoder) to represent the retrieved target and the ground truth with latent vectors, and then diminishes the bias by decreasing the distance of two corresponding distributions. The extensive experiments on two tasks show that the proposed methods excel the existing state-of-the-art generation models.