نقدم المساهمة المشتركة في IST و Grongel بمهمة WMT 2021 المشتركة بشأن تقدير الجودة.شارك فريقنا في مهمتين: التقييم المباشر وجهد التحرير بعد، يشمل ما مجموعه 35 تقريرا.بالنسبة لجميع التقديمات، ركزت جهودنا على تدريب النماذج متعددة اللغات على رأس الهندسة المعمارية المتنبئة ل OpenKiwi، باستخدام ترميزات متعددة اللغات المدربة مسبقا جنبا إلى جنب مع المحولات.نؤدي إلى مزيد من التجربة والأهداف والميزات المرتبطة بعدم اليقين بالإضافة إلى التدريب على بيانات التقييم المباشر خارج المجال.
We present the joint contribution of IST and Unbabel to the WMT 2021 Shared Task on Quality Estimation. Our team participated on two tasks: Direct Assessment and Post-Editing Effort, encompassing a total of 35 submissions. For all submissions, our efforts focused on training multilingual models on top of OpenKiwi predictor-estimator architecture, using pre-trained multilingual encoders combined with adapters. We further experiment with and uncertainty-related objectives and features as well as training on out-of-domain direct assessment data.
References used
https://aclanthology.org/
In this paper, we present the joint contribution of Unbabel and IST to the WMT 2021 Metrics Shared Task. With this year's focus on Multidimensional Quality Metric (MQM) as the ground-truth human assessment, our aim was to steer COMET towards higher c
We report the results of the WMT 2021 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels. This edition focused on two main novel additio
This paper describes Papago submission to the WMT 2021 Quality Estimation Task 1: Sentence-level Direct Assessment. Our multilingual Quality Estimation system explores the combination of Pretrained Language Models and Multi-task Learning architecture
In this paper, we introduce the Eval4NLP-2021 shared task on explainable quality estimation. Given a source-translation pair, this shared task requires not only to provide a sentence-level score indicating the overall quality of the translation, but
This paper presents the JHU-Microsoft joint submission for WMT 2021 quality estimation shared task. We only participate in Task 2 (post-editing effort estimation) of the shared task, focusing on the target-side word-level quality estimation. The tech