Machine Translation Evaluation Meets Community Question Answering
Table 1: Main results on the ranking task.
['[BOLD] System', '[BOLD] MAP', '[BOLD] AvgRec', '[BOLD] MRR']
We can see that the vanilla MTE system (MTEvanilla), which only uses features from our original MTE model, i.e., it does not have any task-specific features (Task_Features and QL_vectors), performs surprisingly well despite the differences in the MTE and cQA tasks. It outperforms a random baseline (Baselinerand) and a chronological baseline that assumes that early comments are better than later ones (Baselinetime) by large margins: by about 11 and 17 MAP points absolute, respectively. For the other two measures the results are similar.