[PDF] Top 20 Prompsit’s submission to WMT 2018 Parallel Corpus Filtering shared task
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Prompsit’s submission to WMT 2018 Parallel Corpus Filtering shared task
... Concerning our submissions, results show that adding n-gram saturation (prompsit-sat) slightly improves the results in the four datasets, which confirms that vocabulary diversity is rele- vant for this ... See full document
8
Alibaba Submission to the WMT18 Parallel Corpus Filtering Task
... The parallel corpus is an essential resource for machine translation and multilingual natural lan- guage ...of parallel corpus is also very important in MT system training (Koehn and Knowles, ... See full document
6
The University of Helsinki Submission to the WMT19 Parallel Corpus Filtering Task
... the corpus filtering task organizers de- cided to pose the problem under more challeng- ing conditions by focusing on low-resource sce- narios, as opposed to previous year German– English (Koehn et ... See full document
7
UTFPR at WMT 2018: Minimalistic Supervised Corpora Filtering for Machine Translation
... In this contribution, we presented the UTFPR sys- tems submitted to the WMT 2018 parallel corpus filtering task. Our supervised systems discern be- tween good and bad ... See full document
5
STACC, OOV Density and N gram Saturation: Vicomtech’s Participation in the WMT 2018 Shared Task on Parallel Corpus Filtering
... the original approach with a simple method based on the number of unknown words, to tackle the significant amounts of noise featured in the corpus filtering task. Additionally, we experimented with a ... See full document
7
The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task
... As neural network-based translation model we use the transformer architecture (Vaswani et al., 2017) implemented in the Sockeye toolkit (Hieber et al., 2017) which is build on top of MXNet (Chen et al., 2015). Encoder ... See full document
9
Findings of the WMT 2018 Shared Task on Automatic Post Editing
... of parallel attention layers (4 and 8 ...the WMT‘17 Trans- lation task (Huck et ...the task, training is per- formed by taking advantage of both the artificial data provided by ... See full document
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NICT’s Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task
... WMT18 shared parallel corpus filtering ...German-English corpus crawled from the web as part of the Paracrawl project. This corpus is too noisy to build an acceptable neural ma- ... See full document
5
NRC Parallel Corpus Filtering System for WMT 2019
... In this paper, we presented the NRC’s submissions to the WMT19 parallel corpus filtering task. Offi- cial results indicate our best systems were ranked 3rd or 4th out of over 20 submissions in ... See full document
9
Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron
... We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data. Our method connects autoencoding and neural machine ... See full document
11
PROMT Systems for WMT 2018 Shared Translation Task
... translation. We see two reasons for that: first, we lose precision because frequently a name, even translated correctly, appears in the wrong case in the output. Russian is a highly inflective language and this is a ... See full document
5
JU Saarland Submission to the WMT2019 English–Gujarati Translation Shared Task
... in WMT 2019. We initially used monoses (Artetxe et al., 2018), which is based on unsupervised statistical phrase based machine translation, to translate the monolingual sentences from English to ... See full document
6
SYSTRAN Participation to the WMT2018 Shared Task on Parallel Corpus Filtering
... Corpus-based approaches to machine translation rely on the availability and quality of parallel cor- pora. In the case of neural machine translation, a large neural network is trained to maximise the ... See full document
5
Noisy Parallel Corpus Filtering through Projected Word Embeddings
... the WMT 2019 par- allel corpus filtering shared task is to select the 5 million words of parallel sentences producing the highest-quality machine translation system, given a set ... See full document
5
Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering
... As Rarrick et al. (2011) point out, one type of noise in parallel corpora extracted from the web are translations that have been created by machine translation. Venugopal et al. (2011) propose a method to ... See full document
14
Webinterpret Submission to the WMT2019 Shared Task on Parallel Corpus Filtering
... The filtering rules we implemented for our sub- mission are not language specific, and moreover, they only place very mild assumption on what con- stitutes a ”good” sentence ... See full document
6
Coverage and Cynicism: The AFRL Submission to the WMT 2018 Parallel Corpus Filtering Task
... Optimizing the heuristic and empirical prefilter- ing and preprocessing steps given here could yield substantial benefit. We have doubtlessly removed some beneficial lines in the prefiltering, which ex- cluded up to 90% ... See full document
5
MAJE Submission to the WMT2018 Shared Task on Parallel Corpus Filtering
... We also conducted some initial experiments us- ing the Common Crawl corpus, under the rationale that it would be closer to the domain of the noisy data from the Paracrawl corpus. However, Com- mon Crawl ... See full document
5
The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task
... By comparing the results of the two alternative ranking schemes, we conclude that their perfor- mances are similar for the 100M corpora. This is explained by the fact that their intersection is ex- tremely high: 5.2M ... See full document
6
Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low Resource Conditions
... Use of embeddings. While the participant’s methods were dominated by non-neural compo- nents, sometimes using neural machine transla- tion outputs and scores, some participants used word and sentence embeddings. Given ... See full document
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