Articles

In this paper the UHH submission to the WMT17 Metrics Shared Task is presented, which is based on sequence and tree ker- nel functions applied to the reference and candidate translations. In addition we also explore the effect of applying the ker- nel functions on the source sentence and a …...

We present a new model for text re- gression that seamlessly combine engi- neered features and character-level infor- mation through deep parallel convolution stacks, multi-layer perceptrons and multi- task learning. We use these models to create the SHEF/CNN systems for the sentence-level Quality Estimation task of WMT 2017 and Emotion …...

This paper presents the contribution of the Unbabel team to the WMT 2017 Shared Task on Translation Quality Estimation. We participated on the word-level and sentence-level tracks. We describe our two submitted systems: (i) STACKEDQE, a “pure” QE system, trained only on the provided training sets, which is a stacked …...

The field of Quality Estimation (QE) has the goal to provide automatic methods for the evaluation of Machine Translation (MT), that do not require reference transla- tions in their computation. We present our submission to the sentence level WMT17 Quality Estimation Shared Task. It com- bines tree and sequence kernels …...

Machine translation quality estimation is a challenging task in the WMT evaluation campaign. Feature extrac- tion plays an important role in auto- matic quality estimation, and in this paper, we propose neural network fea- tures, including embedding features and cross-entropy features of source sentences and machine translations, to improve machine …...

This paper describes the SHEF submis- sions for the three sub-tasks of the Qual- ity Estimation shared task of WMT17, namely: (i) a word-level prediction sys- tem using bilexical embeddings, (ii) a phrase-level labelling approach based on the word-level predictions, (iii) a sentence- level prediction system using word em- beddings …...

Referential translation machines achieve top performance in both bilingual and monolingual settings without accessing any task or domain specific information or resource. RTMs achieve the 3rd system re- sults for German to English sentence-level prediction of translation quality and the 2nd system results according to root mean squared error. In …...

This submission investigates alternative machine learning models for predicting the HTER score on the sentence level. Instead of directly predicting the HTER score, we suggest a model that jointly pre- dicts the amount of the 4 distinct post- editing operations, which are then used to calculate the HTER score. This …...

This paper presents the results of the WMT17 Neural MT Training Task. The objective of this task is to explore the methods of training a fixed neural ar- chitecture, aiming primarily at the best translation quality and, as a secondary goal, shorter training time. Task par- ticipants were provided with …...

We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was orga- nized jointly by Amazon and Heidelberg University for the first time at the Sec- ond Conference on Machine Translation (WMT 2017). The goal of the task is to encourage …...

This paper presents the results of the WMT17 Metrics Shared Task. We asked participants of this task to score the out- puts of the MT systems involved in the WMT17 news translation task and Neu- ral MT training task. We collected scores of 14 metrics from 8 research groups. In …...

We present in this paper the participation of the University of Hamburg in the Biomedical Translation Task of the Sec- ond Conference on Machine Translation (WMT 2017). Our contribution lies in adopting a new direction for perform- ing data selection for Machine Transla- tion via Paragraph Vector and a Feed …...

This paper describes the NICT-NAIST system for the WMT 2017 shared multi- modal machine translation task for both language pairs, English-to-German and English-to-French. We built a hierarchical phrase-based (Hiero) translation system and trained an attentional encoder-decoder neural machine translation (NMT) model to rerank the n-best output of the Hiero system, …...

In this paper, we present a two-stage neu- ral quality estimation model that uses mul- tilevel task learning for translation quality estimation (QE) at the sentence, word, and phrase levels. Our approach is based on an end-to-end stacked neural model named Predictor-Estimator, which has two stages consisting of a neural …...

This paper describes the University of Sheffield’s submission to the WMT17 Multimodal Machine Translation shared task. We participated in Task 1 to de- velop an MT system to translate an im- age description from English to German and French, given its corresponding im- age. Our proposed systems are based on …...

This paper describes Oregon State Uni- versity’s submissions to the shared WMT’17 task “multimodal translation task I”. In this task, all the sentence pairs are image captions in different languages. The key difference be- tween this task and conventional ma- chine translation is that we have cor- responding images as …...

In this paper we explore several neural network architectures for the WMT 2017 multimodal translation sub-task on mul- tilingual image caption generation. The goal of the task is to generate image cap- tions in German, using a training corpus of images with captions in both English and German. We explore …...

In this paper, we describe our submissions to the WMT17 Multimodal Translation Task. For Task 1 (multimodal translation), our best scoring system is a purely textual neural translation of the source image cap- tion to the target language. The main fea- ture of the system is the use of additional …...

This paper introduces the AFRL-OSU Multimodal Machine Translation Task 1 system for submission to the Conference on Machine Translation 2017 (WMT17). This is an atypical MT system in that the image is the catalyst for the MT results, and not the textual content. 1...

We report experiments with multi-modal neural machine translation models that in- corporate global visual features in differ- ent parts of the encoder and decoder, and use the VGG19 network to extract fea- tures for all images. In our experiments, we explore both different strategies to in- clude global image features …...