Báo cáo khoa học: "Better Word Alignments with Supervised ITG Models"
33
lượt xem 4
download
lượt xem 4
download
Download
Vui lòng tải xuống để xem tài liệu đầy đủ
This work investigates supervised word alignment methods that exploit inversion transduction grammar (ITG) constraints. We consider maximum margin and conditional likelihood objectives, including the presentation of a new normal form grammar for canonicalizing derivations. Even for non-ITG sentence pairs, we show that it is possible learn ITG alignment models by simple relaxations of structured discriminative learning objectives. For efficiency, we describe a set of pruning techniques that together allow us to align sentences two orders of magnitude faster than naive bitext CKY parsing. ...
Chủ đề:
Bình luận(0) Đăng nhập để gửi bình luận!
CÓ THỂ BẠN MUỐN DOWNLOAD