Whitepaper for Shared Task on Learning Reordering from Word Alignments at RSMT 2012

Report of the Shared Task on Learning Reordering from Word Alignments at RSMT 2012

Word Alignment-Based Reordering of Source Chunks in PB-SMT

Reordering poses a big challenge in statistical machine translation between distant language pairs. The paper presents how reordering between distant language pairs can be handled efficiently in phrase-based statistical …

Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation

Non-autoregressive translation (NAT) models are typically trained with the cross-entropy loss, which forces the model outputs to be aligned verbatim with the target sentence and will highly penalize small shifts in word …

Neural Reordering Model Considering Phrase Translation and Word Alignment for Phrase-based Translation

This paper presents an improved lexicalized reordering model for phrase-based statistical machine translation using a deep neural network. Lexicalized reordering suffers from reordering ambiguity, data sparseness and noi…

A Study of Word-Classing for MT Reordering

MT systems typically use parsers to help reorder constituents. However most languages do not have adequate treebank data to learn good parsers, and such training data is extremely time-consuming to annotate. Our earlier …