Automatic text summarization systems commonly involve humans for preparing data or
evaluating model performance, yet, there lacks a systematic understanding of humans' roles …

Neural machine translation (NMT) has obtained significant performance improvement over
the recent years. However, NMT models still face various challenges including fragility and …

We study the problem of online learning with human feedback in the human-in-the-loop
machine translation, in which the human translators revise the machine-generated …

Automatic machine translation is super efficient to produce translations yet their quality is not
guaranteed. This technique report introduces TranSmart, a practical human-machine …

Computer-aided translation (CAT), the use of software to assist a human translator in the
translation process, has been proven to be useful in enhancing the productivity of human …

Previous studies have shown that neural machine translation (NMT) models can benefit from
explicitly modeling translated (Past) and untranslated (Future) to groups of translated and …

We propose a touch-based editing method for translation, which is more flexible than
traditional keyboard-mouse-based translation postediting. This approach relies on touch …

Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted Translation.
It aims at providing word-level auto-completion suggestions for human translators. While …

Summarizing lengthy documents is a common and essential task in our daily lives. Although
recent advancements in neural summarization models can assist in crafting general …

Neural machine translation (NMT) systems often produce inadequate translations for named
entities. In this study, we conducted preliminary experiments to examine the factors affecting …