Multilingual Grammar Induction with Continuous Language Identification Wenjuan Han, Ge Wang , Yong Jiang, Kewei Tu ShanghaiTech University, Shanghai, China Alibaba Group {hanwj,wangge,tukw}@shanghaitech.edu.cn {yongjiang.jy}@alibaba-inc.com November 9, 2019 Han et al., 2019 M-NDMV November 9, 2019 1 / 24
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Multilingual Grammar Induction with ContinuousLanguage Identification
Wenjuan Han, Ge Wang, Yong Jiang, Kewei Tu
ShanghaiTech University, Shanghai, ChinaAlibaba Group
Grammar induction is the task to learn grammars form unannotatedcorpus.
Han et al., 2019 M-NDMV November 9, 2019 4 / 24
Motivation
Grammar induction is the task to learn grammars form unannotatedcorpus.
Multilingual grammar induction couples grammar parameters ofdifferent languages together and learns them simultaneously.
Han et al., 2019 M-NDMV November 9, 2019 5 / 24
Motivation
Grammar induction is the task to learn grammars form unannotatedcorpus.
Multilingual grammar induction couples grammar parameters ofdifferent languages together and learns them simultaneously.
→ The key is to exploit the similarities between languages.
Han et al., 2019 M-NDMV November 9, 2019 6 / 24
Motivation
Existing approaches to tackle this problem:
Treating languages equally (Iwata et al., 2010).
Utilizing hand-crafted phylogenetic tree to encode this kind ofinformation (Berg-Kirkpatrick and Klein, 2010).
Han et al., 2019 M-NDMV November 9, 2019 7 / 24
Motivation
Existing approaches to tackle this problem:
Treating languages equally (Iwata et al., 2010). → Languagesimilarity ignored.
Utilizing hand-crafted phylogenetic tree to encode this kind ofinformation (Berg-Kirkpatrick and Klein, 2010). → Need linguisticknowledge and sometimes could be misleading. Example: English isdominant SVO while German is not, although they are both Germaniclanguages.
Han et al., 2019 M-NDMV November 9, 2019 8 / 24
Model
Outline
1 Motivation
2 Model
3 Learning
4 Experiments
5 Conclusion
Han et al., 2019 M-NDMV November 9, 2019 9 / 24
Model
We represent language identities with continuous vectors i.e., languageembeddings and use them to encode language similarity.