Grayscale Data Construction and Multi-Level Ranking Objective for Dialogue Response Selection

Response selection plays a vital role in building retrieval-based conversation systems. Recent works on enhancing response selection mainly focus on inventing new neural architectures for better modeling the relation between dialogue context and response candidates. In almost all these previous works, binary-labeled training data are assumed: Every response candidate is either positive (relevant) or negative (irrelevant). We propose to automatically build training data with grayscale labels. To make full use of the grayscale training data, we propose a multi-level ranking strategy. Experimental results on two benchmark datasets show that our new training strategy significantly improves performance over existing state-of-the-art matching models in terms of various evaluation metrics.