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| author | TharinduDR <rhtdranasinghe@gmail.com> | 2021-03-19 22:17:45 +0300 |
|---|---|---|
| committer | TharinduDR <rhtdranasinghe@gmail.com> | 2021-03-19 22:17:45 +0300 |
| commit | a360be2a6d087bea72201aa535b20f989f12318a (patch) | |
| tree | 0888af37315464f85e6d493c661f7d25777083e9 | |
| parent | 2be7f2c8ec94f76575d46a088aa9b67a5f4935e5 (diff) | |
056: Code Refactoring
| -rw-r--r-- | README.md | 1 | ||||
| -rw-r--r-- | docs/index.md | 3 | ||||
| -rw-r--r-- | mkdocs.yml | 2 |
3 files changed, 5 insertions, 1 deletions
@@ -1,3 +1,4 @@ +[](https://opensource.org/licenses/Apache-2.0) [](https://pepy.tech/project/transquest) # TransQuest: Translation Quality Estimation with Cross-lingual Transformers The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level. diff --git a/docs/index.md b/docs/index.md index 278364e..5e3af19 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,4 +1,7 @@ # TransQuest: Translation Quality Estimation with Cross-lingual Transformers + +[](https://opensource.org/licenses/Apache-2.0) [](https://pepy.tech/project/transquest) + The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level. With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest). @@ -4,7 +4,7 @@ nav: - Home: index.md - TransQuest Architectures: - Sentence-level: architectures/sentence_level_architectures.md - - Word-level: architectures/word_level_architectures.md + - Word-level: architectures/word_level_architecture.md - Examples: - Sentence-level: examples/sentence_level_examples.md - Word-level: examples/word_level_examples.md |