056: Code Refactoringv1.0.0-beta1.0.0

2 files changed, 57 insertions, 70 deletions

diff --git a/README.md b/README.md
index 8031223..7ec4361 100644
--- a/README.md
+++ b/README.md
@@ -1,56 +1,41 @@
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Downloads](https://pepy.tech/badge/transquest)](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.
 
-# TransQuest : Translation Quality Estimation with Cross-lingual Transformers. 
+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).
 
-TransQuest provides state-of-the-art models for translation quality estimation.
-
-We are the winning solution in WMT 2020 Quality Estimation Shared Task -  Sentence-Level Direct Assessment.
-
-Official Documentation is available on - https://tharindudr.github.io/TransQuest.
-
-### Features
+## Features
 - Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
+- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
 - Perform significantly better than current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented. 
 - Pre-trained quality estimation models for fifteen language pairs.  
 
-## Installation
-You first need to install PyTorch. The recommended PyTorch version is 1.5.
-Please refer to [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.
-
-When PyTorch has been installed, you can install TransQuest from source or from pip. 
-
-#### From pip
+## Table of Contents
+1. [Installation](https://tharindudr.github.io/TransQuest/install/) 
+Install TransQuest locally using pip. 
+2. Architectures
+Checkout the architectures implemented in TransQuest
+    1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
+    2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation. 
+3. Examples
+We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
+    1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/sentence_level_examples/)
+    2. [Word-level Examples](https://tharindudr.github.io/TransQuest/word_level_examples/)
+4. Pre-trained Models
+We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
+    1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/sentence_level_pretrained/)
+    2. [Sentence-level Models](https://tharindudr.github.io/TransQuest/sentence_level_pretrained/)
+5. [Contact](https://tharindudr.github.io/TransQuest/contact/)
+Contact us for any issues with TransQuest
+
+## Resources
+- [Research Seminar](https://youtu.be/xbsbHUVVF3s) done on 1st of October 2020 in [RGCL](http://rgcl.wlv.ac.uk/2020/09/24/research-seminar/) and the [slides](https://www.slideshare.net/TharinduRanasinghe1/transquest-238713809).
 
-```bash
-pip install transquest
-```
-
-#### From Source
-```bash
-git clone https://github.com/TharinduDR/TransQuest.git
-cd TransQuest
-pip install -r requirements.txt
-```
-
-Steps that you need to follow after installing TransQuest is available here - https://tharindudr.github.io/TransQuest/architectures/.
-
-## Run the examples
-We have included several examples on how to use TransQuest on recent WMT QE shared tasks. 
-
-1. [WMT 2020 Sentence-level Direct Assessment QE Shared Task](examples/sentence_level/wmt_2020)
-2. [WMT 2020 Sentence-level Post-Editing Effort QE Shared Task](examples/sentence_level/wmt_2020_task2)
-3. [WMT 2019 Sentence-level Post-Editing Effort QE Shared Task](examples/sentence_level/wmt_2019)
-3. [WMT 2018 Sentence-level Post-Editing Effort QE Shared Task](examples/sentence_level/wmt_2018)
-
-More details of the examples can be found in https://tharindudr.github.io/TransQuest/examples/.
-
-## TransQuest Model Zoo
-We released several pre-trained models. You will be able to find them here - https://tharindudr.github.io/TransQuest/pretrained/. We will be keep releasing new pre-trained model. Please keep in touch.
 
 ## Citations
-Please consider citing us if you use the library. 
+If you are using the package, please consider citing this paper which is accepted to [COLING 2020](https://coling2020.org/)
+
 ```bash
-@InProceedings{transquest:2020,
+@InProceedings{transquest:2020a,
 author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
 title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
 booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
@@ -58,12 +43,15 @@ year = {2020}
 }
 ```
 
-The task specific paper on 2020 WMT sentence-level DA that won the first place in the competition. 
+If you are using the task specific fine tuning, please consider citing this which is accepted to [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
+ 
 ```bash
-@InProceedings{transquest:2020,
+@InProceedings{transquest:2020b,
 author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
 title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
 booktitle = {Proceedings of the Fifth Conference on Machine Translation},
 year = {2020}
 }
-```
\ No newline at end of file
+```
+
+
diff --git a/docs/index.md b/docs/index.md
index ada2601..7ec4361 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -3,30 +3,29 @@ The goal of quality estimation (QE) is to evaluate the quality of a translation
 
 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).
 
-
-## Installation
-You first need to install PyTorch. The recommended PyTorch version is 1.6.
-Please refer to [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.
-
-When PyTorch has been installed, you can install TransQuest from source or from pip.
-
-
-### From pip
-
-```bash
-pip install transquest
-```
-
-### From Source
-
-```bash
-git clone https://github.com/TharinduDR/TransQuest.git
-cd TransQuest
-pip install -r requirements.txt
-```
-
-!!! warning 
-We recommend installing TransQuest with pip since it will be more stable. Also make sure that you are working with the latest version available in pip. 
+## Features
+- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
+- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
+- Perform significantly better than current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented. 
+- Pre-trained quality estimation models for fifteen language pairs.  
+
+## Table of Contents
+1. [Installation](https://tharindudr.github.io/TransQuest/install/) 
+Install TransQuest locally using pip. 
+2. Architectures
+Checkout the architectures implemented in TransQuest
+    1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
+    2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation. 
+3. Examples
+We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
+    1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/sentence_level_examples/)
+    2. [Word-level Examples](https://tharindudr.github.io/TransQuest/word_level_examples/)
+4. Pre-trained Models
+We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
+    1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/sentence_level_pretrained/)
+    2. [Sentence-level Models](https://tharindudr.github.io/TransQuest/sentence_level_pretrained/)
+5. [Contact](https://tharindudr.github.io/TransQuest/contact/)
+Contact us for any issues with TransQuest
 
 ## Resources
 - [Research Seminar](https://youtu.be/xbsbHUVVF3s) done on 1st of October 2020 in [RGCL](http://rgcl.wlv.ac.uk/2020/09/24/research-seminar/) and the [slides](https://www.slideshare.net/TharinduRanasinghe1/transquest-238713809).