Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userUsing CollGram to Compare Formulaic Language in Human and Machine Translation
باستخدام Collgram لمقارنة اللغة الصيغة في الترجمة الإنسانية والآلية
371
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تبين مقارنة تسلسلات الصيغة في الآلات البشرية والآلة العصبية لمقالات الصحف عالية الجودة أن ترجمات الآلات العصبية تحتوي على تسلسل أقل تردد أقل، ولكن تسلسل صيغة صيغة صينية مرتبطة بشدة (FSS)، والمزيد من FSS عالية التردد.يمكن أن ترتبط هذه الملاحظات بالاختلافات بين المتعلمين من اللغات الثانية من المستويات المختلفة وبين النصوص المترجمة وغير الترجمة.تشير المقارنة بين أنظمة الترجمة الآلية العصبية إلى أن بعض الأنظمة تنتج المزيد من الأقمار الصناعية لكلا النوعين من الأنظمة الأخرى.
A comparison of formulaic sequences in human and neural machine translation of quality newspaper articles shows that neural machine translations contain less lower-frequency, but strongly-associated formulaic sequences (FSs), and more high-frequency FSs. These observations can be related to the differences between second language learners of various levels and between translated and untranslated texts. The comparison between the neural machine translation systems indicates that some systems produce more FSs of both types than other systems.
References used
https://aclanthology.org/
rate research
Read More
This paper discusses a classification-based approach to machine translation evaluation, as opposed to a common regression-based approach in the WMT Metrics task. Recent machine translation usually works well but sometimes makes critical errors due to
just a few wrong word choices. Our classification-based approach focuses on such errors using several error type labels, for practical machine translation evaluation in an age of neural machine translation. We made additional annotations on the WMT 2015-2017 Metrics datasets with fluency and adequacy labels to distinguish different types of translation errors from syntactic and semantic viewpoints. We present our human evaluation criteria for the corpus development and automatic evaluation experiments using the corpus. The human evaluation corpus will be publicly available upon publication.
In this paper we introduce a vision towards establishing the Malta National Language Technology Platform; an ongoing effort that aims to provide a basis for enhancing Malta's official languages, namely Maltese and English, using Machine Translation.
This will contribute towards the current niche of Language Technology support for the Maltese low-resource language, across multiple computational linguistics fields, such as speech processing, machine translation, text analysis, and multi-modal resources. The end goals are to remove language barriers, increase accessibility, foster cross-border services, and most importantly to facilitate the preservation of the Maltese language.
Resin based composite considers substantially as a restorative
material in clinic. Dentists concern in placement resin composite
restorations with Long term survival.
Materials and Methods: 20 Non carious human third molars, class I
cavity was pr
epared in every molar, restored with methacrylate
resin composite Tetric EvoCeram. The restored teeth were
subjected to thermocycles. Specimen was randomly divided into
two groups : the first group 10 restorations, were repaired by
Cojet® (3M ESPE, Germany) and restored by Filtek™ P90, the
second group 10 restorations also repaired by Cojet ® but restored
by Filtek™ Z350 XT. After that all teeth were subjected to
thermocycles again, later immersed in 0.5% basic fuchsin solution
for 1 s. The teeth were transversely sectioned and observed under
a stereomicroscope. The degree of dye penetration was recorded
and analyzed.
Results: No statistically significant differences in microleakage
were observed between two groups either with low shrinkage
Silorane resin composite or methacrylate resin composite
In this paper, we describe our submissions for the Similar Language Translation Shared Task 2021. We built 3 systems in each direction for the Tamil ⇐⇒ Telugu language pair. This paper outlines experiments with various tokenization schemes to train statistical models. We also report the configuration of the submitted systems and results produced by them.
In this work, two Neural Machine Translation (NMT) systems have been developed and evaluated as part of the bidirectional Tamil-Telugu similar languages translation subtask in WMT21. The OpenNMT-py toolkit has been used to create quick prototypes of
the systems, following which models have been trained on the training datasets containing the parallel corpus and finally the models have been evaluated on the dev datasets provided as part of the task. Both the systems have been trained on a DGX station with 4 -V100 GPUs. The first NMT system in this work is a Transformer based 6 layer encoder-decoder model, trained for 100000 training steps, whose configuration is similar to the one provided by OpenNMT-py and this is used to create a model for bidirectional translation. The second NMT system contains two unidirectional translation models with the same configuration as the first system, with the addition of utilizing Byte Pair Encoding (BPE) for subword tokenization through the pre-trained MultiBPEmb model. Based on the dev dataset evaluation metrics for both the systems, the first system i.e. the vanilla Transformer model has been submitted as the Primary system. Since there were no improvements in the metrics during training of the second system with BPE, it has been submitted as a contrastive system.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
