37 research outputs found
CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks
Neural sequence to sequence learning recently became a very promising
paradigm in machine translation, achieving competitive results with statistical
phrase-based systems. In this system description paper, we attempt to utilize
several recently published methods used for neural sequential learning in order
to build systems for WMT 2016 shared tasks of Automatic Post-Editing and
Multimodal Machine Translation.Comment: Accepted to the First Conference of Machine Translation (WMT16
Findings of the 2017 Conference on Machine Translation
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 run-time estimation
of MT quality), an automatic
post-editing task, a neural MT training
task, and a bandit learning task
CUNI System for the WMT18 Multimodal Translation Task
We present our submission to the WMT18 Multimodal Translation Task. The main
feature of our submission is applying a self-attentive network instead of a
recurrent neural network. We evaluate two methods of incorporating the visual
features in the model: first, we include the image representation as another
input to the network; second, we train the model to predict the visual features
and use it as an auxiliary objective. For our submission, we acquired both
textual and multimodal additional data. Both of the proposed methods yield
significant improvements over recurrent networks and self-attentive textual
baselines.Comment: Published at WMT1
Findings of the 2017 Conference on Machine Translation (WMT17)
This paper presents the results of theWMT17 shared tasks, which included three machine translation (MT) tasks(news, biomedical, and multimodal), two evaluation tasks (metrics and run-time estimation of MT quality), an automatic post-editing task, a neural MT training task, and a bandit learning task
A Shared Task on Bandit Learning for Machine Translation
We introduce and describe the results of a novel shared task on bandit
learning for machine translation. The task was organized jointly by Amazon and
Heidelberg University for the first time at the Second Conference on Machine
Translation (WMT 2017). The goal of the task is to encourage research on
learning machine translation from weak user feedback instead of human
references or post-edits. On each of a sequence of rounds, a machine
translation system is required to propose a translation for an input, and
receives a real-valued estimate of the quality of the proposed translation for
learning. This paper describes the shared task's learning and evaluation setup,
using services hosted on Amazon Web Services (AWS), the data and evaluation
metrics, and the results of various machine translation architectures and
learning protocols.Comment: Conference on Machine Translation (WMT) 201
Findings of the 2016 Conference on Machine Translation (WMT16)
This paper presents the results of the
WMT16 shared tasks, which included five
machine translation (MT) tasks (standard
news, IT-domain, biomedical, multimodal,
pronoun), three evaluation tasks (metrics,
tuning, run-time estimation of MT quality),
and an automatic post-editing task
and bilingual document alignment task.
This year, 102 MT systems from 24 institutions
(plus 36 anonymized online systems)
were submitted to the 12 translation
directions in the news translation task. The
IT-domain task received 31 submissions
from 12 institutions in 7 directions and the
Biomedical task received 15 submissions
systems from 5 institutions. Evaluation
was both automatic and manual (relative
ranking and 100-point scale assessments)
Findings of the 2016 Conference on Machine Translation.
This paper presents the results of the
WMT16 shared tasks, which included five
machine translation (MT) tasks (standard
news, IT-domain, biomedical, multimodal,
pronoun), three evaluation tasks (metrics,
tuning, run-time estimation of MT quality),
and an automatic post-editing task
and bilingual document alignment task.
This year, 102 MT systems from 24 institutions
(plus 36 anonymized online systems)
were submitted to the 12 translation
directions in the news translation task. The
IT-domain task received 31 submissions
from 12 institutions in 7 directions and the
Biomedical task received 15 submissions
systems from 5 institutions. Evaluation
was both automatic and manual (relative
ranking and 100-point scale assessments).
The quality estimation task had three subtasks,
with a total of 14 teams, submitting
39 entries. The automatic post-editing task
had a total of 6 teams, submitting 11 entries