Community-based question answering (CQA) websites represent an important
source of information. As a result, the problem of matching the most valuable
answers to their corresponding questions has become an increasingly popular
research topic. We frame this task as a binary (relevant/irrelevant)
classification problem, and present an adversarial training framework to
alleviate label imbalance issue. We employ a generative model to iteratively
sample a subset of challenging negative samples to fool our classification
model. Both models are alternatively optimized using REINFORCE algorithm. The
proposed method is completely different from previous ones, where negative
samples in training set are directly used or uniformly down-sampled. Further,
we propose using Multi-scale Matching which explicitly inspects the correlation
between words and ngrams of different levels of granularity. We evaluate the
proposed method on SemEval 2016 and SemEval 2017 datasets and achieves
state-of-the-art or similar performance.

本研究提出了一种针对社区问答的二分类（相关 / 不相关）策略，采用敌对训练框架缓解标签失衡问题，并使用生成模型迭代地抽样一组具有挑战性的负样本，最终提高模型预测性能。此外，我们使用多尺度匹配方法显式地检查不同粒度级别的单词和 ngram 之间的相关性。在 SemEval 2016 和 SemEval 2017 数据集上进行评估，结果表明该方法达到或接近最佳性能。