Microsoft Word - Lopez Patiño et al Submitted 3rd re-upload

Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation

This paper reports our systems (UT-AKY) submitted in the 3rd Workshop of Asian Translation 2016 (WAT’16) and their results in the English-to-Japanese translation task. Our model is based on the tree-to-sequence Attention-based NMT (ANMT) model proposed by Eriguchi et al. (2016). We submitted two ANMT systems: one with a word-based decoder and the other with a character-based decoder. Experiment...

Linguistic Heuristics in Word Alignment

The IBM statistical machine translation (SMT) models [Brown et al.1993] have been extremely influential in computational linguistics in the past decade. The (arguably) most striking characteristic of the IBM-style SMT models is their total lack of inherent linguistic knowledge. The IBM models demonstrated how much one can do with pure statistical techniques. This has inspired a whole new genera...

ZooKeys Taxonomy shifts up a gear :

Biodiversity is under severe threat. It is estimated that there are untold millions of species on the planet and we have, in the past 250 years and recently with a decreasing number of specialists, described only about 2 million of them.Though this happens with an agonizing low description rate of an estimated number of 20,000 new species per year (Polaszek et al. 2005), digital and genomic res...

Word Re - ordering and DP - based Sear h in Statisti al

ALTN: Word Alignment Features for Cross-lingual Textual Entailment

We present a supervised learning approach to cross-lingual textual entailment that explores statistical word alignment models to predict entailment relations between sentences written in different languages. Our approach is language independent, and was used to participate in the CLTE task (Task#8) organized within Semeval 2013 (Negri et al., 2013). The four runs submitted, one for each languag...