Given the rapid strides in quality made by automated translation since the advent of Neural Machine Translation, questions regarding the need and role of Post-Editing (PE) may need revisiting. This paper discusses this in light of a survey of opinions from two cohorts of post-graduate students of translation. The responses indicate that the role of PE may need further elaboration in terms of aspects such as grammar, lexis and style, with lexis and style being the main sites requiring human intervention. Also, contrary to expectations, responses generally show marked hesitation in considering quasi-texts as final without PE even in case of disposable texts. The discussion here pertains to English-Chinese translation, but may resonate with other language pairs as well.

Data selection bias has been a long-lasting challenge in the machine learning domain, especially in multi-stage recommendation systems, where the distribution of labeled items for model training is very different from that of the actual candidates during inference time. This distribution shift is even more prominent in the context of online advertising where the user base is diverse and the platform contains a wide range of contents. In this paper, we first investigate the data selection bias in the upper funnel (Ads Retrieval) of Pinterest's multi-cascade ads ranking system. We then conduct comprehensive experiments to assess the performance of various state-of-the-art methods, including transfer learning, adversarial learning, and unsupervised domain adaptation. Moreover, we further introduce some modifications into the unsupervised domain adaptation and evaluate the performance of different variants of this modified method. Our online A/B experiments show that the modified version of unsupervised domain adaptation (MUDA) could provide the largest improvements to the performance of Pinterest's advertisement ranking system compared with other methods and the one used in current production.

Pedagogy has undergone a paradigm shift since the focus changed from uni-directional transmission to collaborative construction of knowledge. The social constructivist approach calls for pedagogy to facilitate interaction between learners involved in collaborative problem solving of real life tasks. This paper describes a wiki-based implementation of this approach (TransWiki) in the learning of translation. The paper examines issues that arise both from the perspective of the learner/user and the pedagogue and discusses solutions supported by the customization of the wiki system. User surveys and a case study indicate that the platform for collaboration is generally well received, but there is marked ambivalence with regard to the advantages of asynchronous collaboration through TransWiki over real-time face-to-face discussions. From the perspective of the instructor, the platform is seen as enabling scaffolding and providing a wealth of data that could inform pedagogy.