Natural language understanding  in the context of goal oriented dialog systems typically includes intent classification and slot labeling tasks. An effective method to expand an NLU system to new languages is using machine translation (MT) with annotation projection to the target language. Previous work focused on using word alignment tools or complex heuristics for slot annotation projection . In this work, we propose a novel end-to-end model that learns to align and predict slots. Existing multilingual NLU data sets only support up to three languages which limits the study on cross-lingual transfer. To this end, we construct a multilingual NLU corpus , MultiATIS++, by extending the Multilingual ATIS corpus to nine languages across various language families. We use the corpusto explore various cross-lingual transfer methods focusing on the zero-shot setting and leveraging MT for language expansion . Results show that our soft-alignment method significantly improves slot F1 over strong baselines on most languages . In addition, our experiments show the strength of using multilingual BERT for both cross-lingual training and zero-shot transfer .
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 .
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(NLU)
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goal oriented
goal-oriented
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An effective method
Existing methods
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is using machine translation (MT) with annotation projection to the target language. Previous work focused on using word alignment tools or complex heuristics for slot annotation projection
use machine translation with slot label projection from source to the translated utterances, and thus are sensitive to projection errors
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slots. Existing multilingual NLU data sets only support up to three languages which limits the study on
target slot labels jointly for
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To this end, we construct a
We introduce MultiATIS++, a new
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, MultiATIS++, by extending
that extends
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various language families. We use the corpusto explore various cross-lingual transfer methods focusing on the zero-shot setting and leveraging MT for language expansion
four language families, and evaluate our method using the corpus
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soft-alignment method significantly improves slot F1 over strong baselines
method outperforms a simple label projection method using fast-align
R
. In addition, our experiments show the strength of using multilingual BERT for both cross-lingual training and zero-shot transfer
, 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