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Register a new userFeedback in Online Translation Courses and the Covid Era
ردود الفعل في دورات الترجمة عبر الإنترنت وعصر كوفي
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الترجمة المعاكسة المعاكسة تعاليم على مستوى العالم.يمثل الانتقال القسري للتدريس عبر الإنترنت تحدي غارغانتوي لأي شخص من ذوي الخبرة فقط في تدريس وجها لوجه.يتطلب تعليم الترجمة عبر الإنترنت مناهجا متميزة لضمان أن الطلاب يمكنهم الوصول إلى أهداف التعلم المستهدفة.تقدم هذه الورقة مراجعة الأدبيات حول تقديم ملاحظات فعالة في ضوء هذه التغييرات الجذابة في تدريس الترجمة بالإضافة إلى وصف كمستوى البحث الحالي على التعليقات عبر الإنترنت لتدريب الترجمة على تصميم الدورات التدريبية عبر الإنترنت في برنامج الترجمةفي جامعة روتجرز.
The Covid pandemic upended translation teaching globally. The forced move to online teaching represented a gargantuan challenge for anyone only experienced in face-to-face teaching. Online translation teaching requires distinct approaches to guarantee that students can reach the targeted learning goals. This paper presents a literature review on the provision of effective feedback in the light of these drastic changes in translation teaching as well as a description as how existing research on online feedback for translation training has been applied to the design of online courses at the translation program at Rutgers University.
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Adaptive Machine Translation purports to dynamically include user feedback to improve translation quality. In a post-editing scenario, user corrections of machine translation output are thus continuously incorporated into translation models, reducing
or eliminating repetitive error editing and increasing the usefulness of automated translation. In neural machine translation, this goal may be achieved via online learning approaches, where network parameters are updated based on each new sample. This type of adaptation typically requires higher learning rates, which can affect the quality of the models over time. Alternatively, less aggressive online learning setups may preserve model stability, at the cost of reduced adaptation to user-generated corrections. In this work, we evaluate different online learning configurations over time, measuring their impact on user-generated samples, as well as separate in-domain and out-of-domain datasets. Results in two different domains indicate that mixed approaches combining online learning with periodic batch fine-tuning might be needed to balance the benefits of online learning with model stability.
We present a tool that provides automated feedback to students studying Spanish writing. The feedback is given for four categories: topic development, coherence, writing conventions, and essay organization. The tool is made freely available via a Goo
gle Docs add-on. A small user study with third-level students in Mexico shows that students found the tool generally helpful and that most of them plan to continue using it as they work to improve their writing skills.
In online forums focused on health and wellbeing, individuals tend to seek and give the following social support: emotional and informational support. Understanding the expressions of these social supports in an online COVID- 19 forum is important fo
r: (a) the forum and its members to provide the right type of support to individuals and (b) determining the long term effects of the COVID-19 pandemic on the well-being of the public, thereby informing interventions. In this work, we build four machine learning models to measure the extent of the following social supports expressed in each post in a COVID-19 online forum: (a) emotional support given (b) emotional support sought (c) informational support given, and (d) informational support sought. Using these models, we aim to: (i) determine if there is a correlation between the different social supports expressed in posts e.g. when members of the forum give emotional support in posts, do they also tend to give or seek informational support in the same post? (ii) determine how these social supports sought and given changes over time in published posts. We find that (i) there is a positive correlation between the informational support given in posts and the emotional support given and emotional support sought, respectively, in these posts and (ii) over time, users tended to seek more emotional support and give less emotional support.
Translating text into a language unknown to the text's author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. We demonstrate this by sho
wing three ways in which user confidence in the outbound translation, as well as its overall final quality, can be affected: backward translation, quality estimation (with alignment) and source paraphrasing. In this paper, we describe an experiment on outbound translation from English to Czech and Estonian. We examine the effects of each proposed feedback module and further focus on how the quality of machine translation systems influence these findings and the user perception of success. We show that backward translation feedback has a mixed effect on the whole process: it increases user confidence in the produced translation, but not the objective quality.
With counterfactual bandit learning, models can be trained based on positive and negative feedback received for historical predictions, with no labeled data needed. Such feedback is often available in real-world dialog systems, however, the modulariz
ed architecture commonly used in large-scale systems prevents the direct application of such algorithms. In this paper, we study the feedback attribution problem that arises when using counterfactual bandit learning for multi-domain spoken language understanding. We introduce an experimental setup to simulate the problem on small-scale public datasets, propose attribution methods inspired by multi-agent reinforcement learning and evaluate them against multiple baselines. We find that while directly using overall feedback leads to disastrous performance, our proposed attribution methods can allow training competitive models from user feedback.
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