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A Universal Framework for Inductive Transfer Parsing across Multi typed Treebanks

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Figure 1: Comparisons between multilingual universal dependencies (a) and monolingual heterogeneousdependencies (b).
Figure 2: The LSTM-based neural parser (a) and the Char-BiLSTM for modeling words (b).
Figure 3: Illustration of the MTL framework.
Table 1: Parameter sharing strategies for MULTI-UNIV and MONO-HETERO. LSTM(S) – stack LSTM;LSTM(B) – buffer LSTM; LSTM(A) – action LSTM; BiLSTM(chars) – Char-BiLSTM; RecNN – re-cursive NN modeling the subtrees; WA, W S, W B– weights from A, S, B to the state (pt ); g – weightsfrom the state to output layer; E– embeddings.
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