论文标题
为什么过度拟合并不总是不好：翻新词典嵌入词典
Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries
论文作者
论文摘要
通常在双语词典诱导（BLI）上评估跨语性单词嵌入（CLWE）。最近的CLWE方法使用线性预测（低于培训词典）来推广BLI。但是，拟合不足可能会阻碍其他依赖培训词典中词语的下游任务。我们通过将CLWE改造为训练词典来解决此限制，该词典将培训翻译对嵌入嵌入空间更近，并过度贴上培训词典。尽管降低了BLI测试精度，但这个简单的后处理步骤通常会提高两个下游任务的准确性。我们还将对Clwe引起的训练词典和合成词典进行翻新，这有时会在下游任务上更好地概括。我们的结果证实了在下游任务中充分利用培训词典的重要性，并解释了为什么BLI是一个有缺陷的Claw Wealution。
Cross-lingual word embeddings (CLWE) are often evaluated on bilingual lexicon induction (BLI). Recent CLWE methods use linear projections, which underfit the training dictionary, to generalize on BLI. However, underfitting can hinder generalization to other downstream tasks that rely on words from the training dictionary. We address this limitation by retrofitting CLWE to the training dictionary, which pulls training translation pairs closer in the embedding space and overfits the training dictionary. This simple post-processing step often improves accuracy on two downstream tasks, despite lowering BLI test accuracy. We also retrofit to both the training dictionary and a synthetic dictionary induced from CLWE, which sometimes generalizes even better on downstream tasks. Our results confirm the importance of fully exploiting training dictionary in downstream tasks and explains why BLI is a flawed CLWE evaluation.
