Word discovery is the task of extracting words from unsegmented text. In this
paper we examine to what extent neural networks can be applied to this task in
a realistic unwritten language scenario, where only small corpora and limited
annotations are available. We investigate two scenarios: one with no
supervision and another with limited supervision with access to the most
frequent words. Obtained results show that it is possible to retrieve at least
27% of the gold standard vocabulary by training an encoder-decoder neural
machine translation system with only 5,157 sentences. This result is close to
those obtained with a task-specific Bayesian nonparametric model. Moreover, our
approach has the advantage of generating translation alignments, which could be
used to create a bilingual lexicon. As a future perspective, this approach is
also well suited to work directly from speech.

本研究考察了神经网络在非书面语境下的词语发现任务中的应用，其中包括无监督和有限监督两种情形，结果表明在只有 5,157 个句子的情况下，使用编码器 - 解码器神经机器翻译系统进行训练可以检索出至少 27% 的词汇量。此外，我们的方法具有生成翻译对位的优势，这可用于创建双语词表。未来可以直接从语音输入中使用此方法。