13 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
Retrieve and Refine: Improved Sequence Generation Models For Dialogue
Sequence generation models for dialogue are known to have several problems:
they tend to produce short, generic sentences that are uninformative and
unengaging. Retrieval models on the other hand can surface interesting
responses, but are restricted to the given retrieval set leading to erroneous
replies that cannot be tuned to the specific context. In this work we develop a
model that combines the two approaches to avoid both their deficiencies: first
retrieve a response and then refine it -- the final sequence generator treating
the retrieval as additional context. We show on the recent CONVAI2 challenge
task our approach produces responses superior to both standard retrieval and
generation models in human evaluations
LIG-CRIStAL System for the WMT17 Automatic Post-Editing Task
This paper presents the LIG-CRIStAL submission to the shared Automatic Post-
Editing task of WMT 2017. We propose two neural post-editing models: a
monosource model with a task-specific attention mechanism, which performs
particularly well in a low-resource scenario; and a chained architecture which
makes use of the source sentence to provide extra context. This latter
architecture manages to slightly improve our results when more training data is
available. We present and discuss our results on two datasets (en-de and de-en)
that are made available for the task.Comment: keywords: neural post-edition, attention model
Translating Short Segments with NMT: A Case Study in English-to-Hindi
This paper presents a case study in translating short image captions of the Visual Genome dataset from English into Hindi using out-of-domain data sets of varying size. We experiment with three NMT models: the shallow and deep sequence-to-sequence and the Transformer model as implemented in Marian toolkit. Phrase-based Moses serves as the baseline. The results indicate that the Transformer model outperforms others in the large data setting in a number of automatic metrics and manual evaluation, and it also produces the fewest truncated sentences. Transformer training is however very sensitive to the hyperparameters, so it requires more experimenting. The deep sequence-to-sequence model produced more flawless outputs in the small data setting and it was generally more stable, at the cost of more training iterations.This work has been supported by the grants 18-24210S of the Czech Science Foundation, SVV 260 453 and “Progress” Q18+Q48 of Charles University, and using language resources distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (projects LM2015071 and OP VVV VI CZ.02.1.01/0.0/0.0/16 013/0001781)
Improving Translations by Combining Fuzzy-Match Repair with Automatic Post-Editing
Two of the more predominant technologies that professional translators have at their disposal for improving productivity are machine translation (MT) and computer-aided translation (CAT) tools based on translation memories (TM). When translators use MT, they can use automatic post-editing (APE) systems to automate partof the post-editing work and get further productivity gains. When they use TM-based CAT tools, productivity may improve if they rely on fuzzy-match repair(FMR) methods. In this paper we combine FMR and APE: first a FMR proposal is produced from the translation unit proposed by the TM, then this proposal is further improved by an APE system specially tuned for this purpose. Experiments conducted on the translation of English texts into German show that, with the two combined technologies, the quality of the translations improves up to 23% compared to a pure MT system. The improvement over a pure FMR system is of 16%, showing the effectiveness of our joint solution