Adversarial Training for Community Question Answer Selection Based on Multi-scale Matching
Table 2: Performance on SemEval 2016 dataset. “Contrs” denotes non-primary submission. Note that both [Mihaylova et al.2016] and [Filice et al.2016] utilized meta information (e.g. answers’ positions in threads; whether an answer is written by the author of the question; whether the author of an answer is active in the thread) while our method only relies on textual information.
['Method', 'MAP', 'MRR']
dataset Using Multi-scale Matching model boosts MAP for both direct and adversarial training, and the improvements are more significant than those on SemEval 2017. With adversarial training, our single-scale and multi-scale models are significantly improved, achieving a MAP of 52.09 and 53.38, respectively. Again we list the performances achieved by the generators. It can be seen that the generators significantly outperform random baseline but is slightly worse than IR and chronological ranking based baseline.