论文标题
端到端的插槽对准和跨语义NLU的认可
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
论文作者
论文摘要
自然语言理解（NLU）在面向目标的对话框系统中通常包括意图分类和插槽标签任务。将NLU系统扩展到新语言的现有方法使用机器翻译，并将插槽标签投影从源到翻译的话语，因此对投影错误敏感。在这项工作中，我们提出了一个新颖的端到端模型，该模型学会了与跨语性转移的联合对齐和预测目标插槽标签。我们介绍了Multiatis ++，这是一种新的多语言NLU语料库，将多语言ATIS语料库扩展到跨四个语言家族的九种语言，并使用语料库评估我们的方法。结果表明，我们的方法在大多数语言上使用快速对齐的方法优于一种简单的标签投影方法，并在培训时间的一半中实现了更复杂，最先进的投影方法的竞争性能。我们将Multiatis ++语料库释放到社区，以继续对跨语义NLU的未来研究。
Natural language understanding (NLU) in the context of goal-oriented dialog systems typically includes intent classification and slot labeling tasks. Existing methods to expand an NLU system to new languages use machine translation with slot label projection from source to the translated utterances, and thus are sensitive to projection errors. In this work, we propose a novel end-to-end model that learns to align and predict target slot labels jointly for cross-lingual transfer. We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus. Results show that our method outperforms a simple label projection method using fast-align on most languages, and achieves competitive performance to the more complex, state-of-the-art projection method with only half of the training time. We release our MultiATIS++ corpus to the community to continue future research on cross-lingual NLU.
