HSCJN: A Holistic Semantic Constraint Joint Network for Diverse Response Generation
Table 1: The results of 1-turn response generation on DailyDialog and OpenSubtitles datasets
['[BOLD] Model', '[BOLD] DailyDialog Corpus  [BOLD] BLEU-1', '[BOLD] DailyDialog Corpus  [BOLD] BLEU-2', '[BOLD] DailyDialog Corpus  [BOLD] BLEU-3', '[BOLD] DailyDialog Corpus  [BOLD] BLEU-4', '[BOLD] DailyDialog Corpus  [BOLD] Distinct-1', '[BOLD] DailyDialog Corpus  [BOLD] Distinct-2', '[BOLD] DailyDialog Corpus  [BOLD] Distinct-3']
It is obvious that our HSCJN generates remarkably more distinct unigrams, bigrams and trigrams than all the baselines on both two datasets. Besides, our model achieves the highest BLEU-2/3/4 values on both two datasets, compared with all the baseline models. Confirmed by our experiments, our model achieves an excellent performance in terms of both quality and diversity, regardless of the scale of datasets. HSCJN(w/o ME) and HSCJN (w/o PN) represent the trained models without the maximum entropy regularizer and without the prediction sub-network respectively. On the DailyDialog dataset, HSCJN (w/o ME) generates the most distinct unigrams, bigrams and trigrams among all compared models, and also surpasses all the baseline models on Distinct-n metrics on the OpenSubtitles dataset, indicating that our joint network can obviously generate more diverse responses. HSCJN(w/o ME) also achieves higher BLEU scores, which proves that our joint network can directly capture semantic information to enhance relevance.