This paper presents the results of the WMT17 shared tasks, which included three machine
translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and …

In this paper we apply distributional semantic information to document-level machine
translation. We train monolingual and bilingual word vector models on large corpora and we …

We propose a simple log-bilinear softmax-based model to deal with vocabulary expansion
in machine translation. Our model uses word embeddings trained on significantly large …

We present a simple method to learn continuous representations of dependency
substructures (links), with the motivation of directly working with higher-order, structured …

Discovering important papers in different academic topics is known as topic-sensitive
influential paper discovery. Previous works mainly find the influential papers based on the …

This paper presents a new approach to the problem of cross-lingual dependency parsing,
aiming at leveraging training data from different source languages to learn a parser in a …

This paper describes the TALP–UPC system in the Spanish–English WMT 2016 biomedical
shared task. Our system is a standard phrase-based system enhanced with vocabulary …

This paper describes the SHEF submissions for the three sub-tasks of the Quality Estimation
shared task of WMT17, namely:(i) a word-level prediction system using bilexical …

Word-level machine translation (MT) quality estimation (QE) is usually formulated as the task
of automatically identifying which words need to be edited (either deleted or replaced) in a …

We present a low-rank multi-linear model for the task of solving prepositional phrase
attachment ambiguity (PP task). Our model exploits tensor products of word embeddings …