Word Embedding Based Correlation Model for Question/Answer Matching
Table 3: Performance of different approaches on Baidu Zhidao dataset. IBM-1 denotes that translation probabilities are learned using IBM translation model 1, cos denotes that translation probabilities are calculated through word vector’s cosine-similarity
['Approach', 'Computers & Internet', 'Computers & Internet', 'Education & Science', 'Education & Science', 'Games', 'Games', 'Entertainment & Recreation', 'Entertainment & Recreation']
We find that WEC and WEC+CNN outperform all other models. Furthermore, IBM-1 based models outperform cos-similarity based models. This is possibly is due to the heterogeneity of question and answer, since both WEC and IBM translation model 1 can directly model the word-to-word co-occurrence probability instead of semantic similarity. On both datasets, traditional retrieval models obtain the worst result because they suffer from the lexical gap problem.