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Found 10 papers, 1 papers with code
System Description for Transperfect
In this paper, we describe our participation in the 2021 Workshop on Asian Translation (team ID: tpt_wat).
Estimation vs Metrics: is QE Useful for MT Model Selection?
This paper presents a case study of applying machine translation quality estimation (QE) for the purpose of machine translation (MT) engine selection.
QE Viewer: an Open-Source Tool for Visualization of Machine Translation Quality Estimation Results
QE Viewer is a web-based tool for visualizing results of a Machine Translation Quality Estimation (QE) system.
Selecting the best data filtering method for NMT training
Performance of NMT systems has been proven to depend on the quality of the training data.
Benchmarking ASR Systems Based on Post-Editing Effort and Error Analysis
This paper offers a comparative evaluation of four commercial ASR systems which are evaluated according to the post-editing effort required to reach “publishable” quality and according to the number of errors they produce.
A Comparison of Data Filtering Methods for Neural Machine Translation
With the increasing availability of large-scale parallel corpora derived from web crawling and bilingual text mining, data filtering is becoming an increasingly important step in neural machine translation (NMT) pipelines.
Comparing Multilingual NMT Models and Pivoting
Following recent advancements in multilingual machine translation at scale, our team carried out tests to compare the performance of multilingual models (M2M from Facebook and multilingual models from Helsinki-NLP) with a two-step translation process using English as a pivot language.
ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts
The Google Patents is one of the main important sources of patents information.
Optimising the Machine Translation Post-editing Workflow
In this article, we describe how machine translation is used for post-editing at TransPerfect and the ways in which we optimise the workflow.
