An Empirical Study of Machine Translation for the Shared Task of WMT18

Abstract

This paper describes the Global Tone Communication Co., Ltd.'s submission of the WMT18 shared news translation task. We participated in the English-to-Chinese direction and get the best BLEU (43.8) scores among all the participants. The submitted system focus on data clearing and techniques to build a competitive model for this task. Unlike other participants, the submitted system are mainly relied on the data filtering to obtain the best BLEU score. We do data filtering not only for provided sentences but also for the back translated sentences. The techniques we apply for data filtering include filtering by rules, language models and translation models. We also conduct several experiments to validate the effectiveness of training techniques. According to our experiments, the Annealing Adam optimizing function and ensemble decoding are the most effective techniques for the model training.

Findings of the 2018 Conference on Machine Translation (WMT18)

“…Due to the non-standard way of submission, the system is not considered a regular participant, but an invited/late submission and marked with " " throughout the paper. (Bei et al, 2018) GTCOM-PRIMARY is based on the Transformer "base" model architecture using Marian toolkit, and it also applies some methods that have been proven effective in NMT system, such as BPE, back-translation, right-to-left reranking and ensembling decoding. In this experiment, right-toleft reranking does not help.…”

A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction

“…As a result, the MT field faces various data quality issues such as misalignment and incorrect translations, which may significantly impact translation quality . A straightforward solution is to apply a filtering approach, where noisy data are filtered out and a smaller subset of high-quality sentence pairs is retained (Bei et al, 2018;Junczys-Dowmunt, 2018;Rossenbach et al, 2018). Nevertheless, it is unclear whether such a filtering approach can be successfully applied to GEC, where commonly available datasets tend to be far smaller than those used in recent neural MT research.…”

“…We evaluated the effectiveness of our method over several GEC datasets, and found that it considerably outperformed baseline methods, includ-ing three strong denoising baselines based on a filtering approach, which is a common approach in MT (Bei et al, 2018;Junczys-Dowmunt, 2018;Rossenbach et al, 2018). We further improved the performance by applying task-specific techniques and achieved state-of-the-art performance on the CoNLL-2014, JFLEG, and BEA-2019 benchmarks.…”

GTCOM Neural Machine Translation Systems for WMT19

“…The methods of data filtering by human rules are mainly the same as we did in English to Chinese (Bei et al, 2018) last year, but language models are used to clean all data, including monolingual data, parallel data and synthetic data. We use Marian to train the transformer language model for each language (i.e.…”