Leveraging Semantic Web Search and Browse Sessions for Multi-Turn Spoken Dialog Systems
Table 4: Results for entity extraction on conversational corpora. Number of utterances containing each entity type is shown in the first row. F1 scores (multiplied by 100) are displayed. “-” indicates that result is unavailable. Statistically significant improvements (p<0.01) over all compared systems except Heck et al. [20] are in bold. † denotes statistical significance (p<0.01) over Baseline 1& Baseline 2.
['[EMPTY]', '[BOLD] CB Data Film', '[BOLD] CB Data Director', '[BOLD] CB Data Actor', '[BOLD] CB Data Character', '[BOLD] CB Data Genre', '[BOLD] Netflix Data Film', '[BOLD] Netflix Data Director', '[BOLD] Netflix Data Actor', '[BOLD] Netflix Data Character', '[BOLD] Netflix Data Genre']
Discussion. This shows that our session-based models is able to effectively leverage the behavioral patterns mined from web search log and multi-turn context to improve the entity extraction performance in spoken dialog systems. The performance on genre detection is relatively low. This may due to the different context people use for genres. For example, “Disney” is a genre in “Disney movie”, but is a place name in “Disney world”. Coreference resolution is not addressed in this work, but we believe it could further improve the performance.