State-of-the-art multilingual systems rely on shared vocabularies that
sufficiently cover all considered languages. To this end, a simple and
frequently used approach makes use of subword vocabularies constructed jointly
over several languages. We hypothesize that such vocabularies are suboptimal
due to false positives (identical subwords with different meanings across
languages) and false negatives (different subwords with similar meanings). To
address these issues, we propose Subword Mapping and Anchoring across Languages
(SMALA), a method to construct bilingual subword vocabularies. SMALA extracts
subword alignments using an unsupervised state-of-the-art mapping technique and
uses them to create cross-lingual anchors based on subword similarities. We
demonstrate the benefits of SMALA for cross-lingual natural language inference
(XNLI), where it improves zero-shot transfer to an unseen language without
task-specific data, but only by sharing subword embeddings. Moreover, in neural
machine translation, we show that joint subword vocabularies obtained with
SMALA lead to higher BLEU scores on sentences that contain many false positives
and false negatives.

提出了一种名为 SMALA 的跨语言子词表构建方法，通过使用无监督的映射技术提取子词对齐并基于子词相似性创建跨语言锚点，可用于改善多语言自然语言推理和神经机器翻译等任务。