Word Embedding Based Correlation Model for Question/Answer Matching
Table 2: Performance of different approaches on Yahoo! Answers dataset (WEC denote sentence-level WEC function)
['Approach', 'Travel DCG@1', 'Travel DCG@6', 'Relationships DCG@1', 'Relationships DCG@6', 'Finance DCG@1', 'Finance DCG@6']
Answer dataset. For top candidate answer precision, WEC slightly outperforms the translation probability based models. For candidate answer ranking qualities, WEC outperforms TRLM and TM. By adding CNN into the model, WEC+CNN outperforms all other models. It is possible to interpret that WEC model can perform better than TRLM and TM model, but merely using lexical level information limits its ability in selecting the best answer. Thus, WEC+CNN is able to improve the result by adding syntactical information into the model.