1 research outputs found
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
Multiple sequence to sequence models were used to establish an end-to-end
multi-turns proactive dialogue generation agent, with the aid of data
augmentation techniques and variant encoder-decoder structure designs. A
rank-based ensemble approach was developed for boosting performance. Results
indicate that our single model, in average, makes an obvious improvement in the
terms of F1-score and BLEU over the baseline by 18.67% on the DuConv dataset.
In particular, the ensemble methods further significantly outperform the
baseline by 35.85%.Comment: 7 pages, 3 figures submitted to journa