An Ensemble Dialogue System for Facts-Based Sentence Generation
Table 2: Statics of pre-processed dataset.“s” and “d” denotes subject facts and description facts , respectively.
['[BOLD] Dialogue Dataset', '[BOLD] train', '[BOLD] dev', '[BOLD] test']
The experiment was performed according to the regulations of DSTC7-Task2. We crawled the dialogue dataset from 178 subreddits (subsidiary threads or categories on Reddit). Markdown and special symbols were eliminated from the crawled dialogue dataset, and for the same context, the context-response pair of the highest “response score” was selected. We crawled the facts dataset from 226 information sharing websites, such as Wikipedia. The facts were categorized into Fsubj and Fdesc as mentioned above, up to the top 10 sentences with the highest cosine similarity for each context. For calculating similarity, the average of the Word2Vec output with 256 dimension was used. Note that the Word2Vec model was trained only on the official training datasets according to DSTC-Task2 regulations.