Common Contracts
3 similar Generalized Agreement for Bidirectional Word Alignment contracts
Generalized Agreement for Bidirectional Word AlignmentGeneralized Agreement for Bidirectional Word Alignment • August 12th, 2015
While agreement-based joint training has proven to deliver state-of-the-art align- ment accuracy, the produced word align- ments are usually restricted to one-to- one mappings because of the hard cons- traint on agreement. We propose a ge- neral framework to allow for arbitrary loss functions that measure the disagreement between asymmetric alignments. The loss functions can not only be defined between asymmetric alignments but al- so between alignments and other latent structures such as phrase segmentations. We use a Viterbi EM algorithm to train the joint model since the inference is intractable. Experiments on Chinese- English translation show that joint training with generalized agreement achieves sig- nificant improvements over two state-of- the-art alignment methods.
Generalized Agreement for Bidirectional Word AlignmentGeneralized Agreement for Bidirectional Word Alignment • August 12th, 2015
While agreement-based joint training has proven to deliver state-of-the-art align- ment accuracy, the produced word align- ments are usually restricted to one-to- one mappings because of the hard cons- traint on agreement. We propose a ge- neral framework to allow for arbitrary loss functions that measure the disagreement between asymmetric alignments. The loss functions can not only be defined between asymmetric alignments but al- so between alignments and other latent structures such as phrase segmentations. We use a Viterbi EM algorithm to train the joint model since the inference is intractable. Experiments on Chinese- English translation show that joint training with generalized agreement achieves sig- nificant improvements over two state-of- the-art alignment methods.
Generalized Agreement for Bidirectional Word AlignmentGeneralized Agreement for Bidirectional Word Alignment • May 31st, 2015
While agreement-based joint training has proven to deliver state-of-the-art align- ment accuracy, the produced word align- ments are usually restricted to one-to- one mappings because of the hard cons- traint on agreement. We propose a ge- neral framework to allow for arbitrary loss functions that measure the disagreement between asymmetric alignments. The loss functions can not only be defined between asymmetric alignments but al- so between alignments and other latent structures such as phrase segmentations. We use a Viterbi EM algorithm to train the joint model since the inference is intractable. Experiments on Chinese- English translation show that joint training with generalized agreement achieves sig- nificant improvements over two state-of- the-art alignment methods.
