42 research outputs found
An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing
In this work, we explore multiple neural architectures adapted for the task
of automatic post-editing of machine translation output. We focus on neural
end-to-end models that combine both inputs (raw MT output) and
(source language input) in a single neural architecture, modeling directly. Apart from that, we investigate the influence of
hard-attention models which seem to be well-suited for monolingual tasks, as
well as combinations of both ideas. We report results on data sets provided
during the WMT-2016 shared task on automatic post-editing and can demonstrate
that dual-attention models that incorporate all available data in the APE
scenario in a single model improve on the best shared task system and on all
other published results after the shared task. Dual-attention models that are
combined with hard attention remain competitive despite applying fewer changes
to the input.Comment: Accepted for presentation at IJCNLP 201
Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation
We combine two of the most popular approaches to automated Grammatical Error
Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC
based on Neural Machine Translation (NMT). The hybrid system achieves new
state-of-the-art results on the CoNLL-2014 and JFLEG benchmarks. This GEC
system preserves the accuracy of SMT output and, at the same time, generates
more fluent sentences as it typical for NMT. Our analysis shows that the
created systems are closer to reaching human-level performance than any other
GEC system reported so far.Comment: Accepted for oral presentation, research track, short papers, at
NAACL 201
Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing
This paper describes the submission of the AMU (Adam Mickiewicz University)
team to the Automatic Post-Editing (APE) task of WMT 2016. We explore the
application of neural translation models to the APE problem and achieve good
results by treating different models as components in a log-linear model,
allowing for multiple inputs (the MT-output and the source) that are decoded to
the same target language (post-edited translations). A simple string-matching
penalty integrated within the log-linear model is used to control for higher
faithfulness with regard to the raw machine translation output. To overcome the
problem of too little training data, we generate large amounts of artificial
data. Our submission improves over the uncorrected baseline on the unseen test
set by -3.2\% TER and +5.5\% BLEU and outperforms any other system submitted to
the shared-task by a large margin.Comment: Submission to the WMT 2016 shared task on Automatic Post-Editin
MS-UEdin Submission to the WMT2018 APE Shared Task:Dual-Source Transformer for Automatic Post-Editing
This paper describes the Microsoft and University of Edinburgh submission to
the Automatic Post-editing shared task at WMT2018. Based on training data and
systems from the WMT2017 shared task, we re-implement our own models from the
last shared task and introduce improvements based on extensive parameter
sharing. Next we experiment with our implementation of dual-source transformer
models and data selection for the IT domain. Our submissions decisively wins
the SMT post-editing sub-task establishing the new state-of-the-art and is a
very close second (or equal, 16.46 vs 16.50 TER) in the NMT sub-task. Based on
the rather weak results in the NMT sub-task, we hypothesize that
neural-on-neural APE might not be actually useful.Comment: Winning submissions for WMT2018 APE shared tas