A Sequential Matching Framework for Multi-turn Response Selection in Retrieval-based Chatbots
Table 7: Evaluation results in terms of different word embedding sizes.
['[EMPTY]', '[BOLD] Ubuntu Corpus', '[BOLD] Ubuntu Corpus', '[BOLD] Ubuntu Corpus', '[BOLD] Ubuntu Corpus', '[BOLD] Douban Corpus', '[BOLD] Douban Corpus', '[BOLD] Douban Corpus', '[BOLD] Douban Corpus', '[BOLD] Douban Corpus', '[BOLD] Douban Corpus']
We can see that SAN is more sensitive to the word embedding size than SCN. SCN gets stable after the embedding size exceeds 100, while SAN keeps getting improved with the increase of the embedding size. Our explanation to the phenomenon is that SCN transforms word vectors and hidden vectors of GRU to scalars in the similarity matrices by dot products, and thus information in extra dimensions (e.g., entries with indices larger than 100) might be lost; on the other hand, SAN leverages the whole d-dimensional vectors in matching, so the information in the embedding can be exploited more sufficiently.