29 research outputs found
Extended Parallel Corpus for Amharic-English Machine Translation
This paper describes the acquisition, preprocessing, segmentation, and
alignment of an Amharic-English parallel corpus. It will be useful for machine
translation of an under-resourced language, Amharic. The corpus is larger than
previously compiled corpora; it is released for research purposes. We trained
neural machine translation and phrase-based statistical machine translation
models using the corpus. In the automatic evaluation, neural machine
translation models outperform phrase-based statistical machine translation
models.Comment: Accepted to 2nd AfricanNLP workshop at EACL 202
Region-Attentive Multimodal Neural Machine Translation
We propose a multimodal neural machine translation (MNMT) method with semantic image regions called region-attentive multimodal neural machine translation (RA-NMT). Existing studies on MNMT have mainly focused on employing global visual features or equally sized grid local visual features extracted by convolutional neural networks (CNNs) to improve translation performance. However, they neglect the effect of semantic information captured inside the visual features. This study utilizes semantic image regions extracted by object detection for MNMT and integrates visual and textual features using two modality-dependent attention mechanisms. The proposed method was implemented and verified on two neural architectures of neural machine translation (NMT): recurrent neural network (RNN) and self-attention network (SAN). Experimental results on different language pairs of Multi30k dataset show that our proposed method improves over baselines and outperforms most of the state-of-the-art MNMT methods. Further analysis demonstrates that the proposed method can achieve better translation performance because of its better visual feature use
Combining SMT and NMT back-translated data for efficient NMT
Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training. Consequently, techniques for augmenting the training set have become popular recently. One of these methods is back-translation (Sennrich et al., 2016), which consists on generating synthetic sentences by translating a set of monolingual, target-language sentences using a Machine Translation (MT) model.
Generally, NMT models are used for back-translation. In this work, we analyze the performance of models when the training data is extended with synthetic data using different MT approaches. In particular we investigate back-translated data generated not only by NMT but also by Statistical Machine Translation (SMT) models and combinations of both. The results reveal that the models achieve the best performances when the training set is augmented with back-translated data created by merging different MT approaches
Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables
Despite the tremendous success of Neural Machine Translation (NMT), its
performance on low-resource language pairs still remains subpar, partly due to
the limited ability to handle previously unseen inputs, i.e., generalization.
In this paper, we propose a method called Joint Dropout, that addresses the
challenge of low-resource neural machine translation by substituting phrases
with variables, resulting in significant enhancement of compositionality, which
is a key aspect of generalization. We observe a substantial improvement in
translation quality for language pairs with minimal resources, as seen in BLEU
and Direct Assessment scores. Furthermore, we conduct an error analysis, and
find Joint Dropout to also enhance generalizability of low-resource NMT in
terms of robustness and adaptability across different domainsComment: Accepted at MT Summit 202
Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models
This work presents a new state of the art in reconstruction of surface
realizations from obfuscated text. We identify the lack of sufficient training
data as the major obstacle to training high-performing models, and solve this
issue by generating large amounts of synthetic training data. We also propose
preprocessing techniques which make the structure contained in the input
features more accessible to sequence models. Our models were ranked first on
all evaluation metrics in the English portion of the 2018 Surface Realization
shared task
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