The goal of meta-learning is to learn to adapt to a new task with only a few labeled
examples. To tackle this problem in NLP, we propose $\textit {in-context tuning} $, which …

We present XHATE-999, a multi-domain and multilingual evaluation data set for abusive
language detection. By aligning test instances across six typologically diverse languages …

Chinese diachronic gap is a key issue in classical Chinese machine reading
comprehension (CCMRC). Preceding work on bridging this gap has been mostly restricted …

We conduct a large-scale fine-grained comparative analysis of machine translations (MTs)
against human translations (HTs) through the lens of morphosyntactic divergence. Across …

Semantic representations capture the meaning of a text. Abstract Meaning Representation
(AMR), a type of semantic representation, focuses on predicate-argument structure and …

We present a method for classifying syntactic errors in learner language, namely errors
whose correction alters the morphosyntactic structure of a sentence. The methodology …

Translation divergences are varied and widespread, challenging approaches that rely on
parallel text. To annotate translation divergences, we propose a schema grounded in the …

We present the task of PreQuEL, Pre-(Quality-Estimation) Learning. A PreQuEL system
predicts how well a given sentence will be translated, without recourse to the actual …

The integration of syntactic structures into Transformer machine translation has shown
positive results, but to our knowledge, no work has attempted to do so with semantic …

Characteristics of the translation product are often used in translation process research as
predictors for cognitive load, and by extension translation difficulty. In the last decade, user …