Soc2Seq: Social Embedding meets Conversation Model
Table 1: Perplexity of location- and user-based models.
['[BOLD] Model', '[BOLD] Perplex.']
We observe that the location-based model results in a significant improvement (about 8% reduction in perplexity), whereas the user-based model is outperformed by the baseline (LSTM) models (about 2% increase in perplexity ). The data sparsity for users may explain this observation, as there is less data per user compared with per location. Moreover, the number of embeddings to be learned for the user-based model (1,00,000) is 10 times more than the corresponding number for location-based model (10,000). We further observe that the decoder-and-encoder model performs worse than the decoder model, which shows that the speaker information does not lead to better suggestions (i.e., replies) in an anonymous environment.