The prompt-based learning paradigm, which bridges the gap between pre-training and fine-
tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

Abstract Machine Translation Quality Estimation (QE) aims to evaluate the quality of
machine translation without relying on references. Recently, Large-scale Language Model …

Recent research shows that pre-trained language models (PLMs) suffer from" prompt bias"
in factual knowledge extraction, ie, prompts tend to introduce biases toward specific labels …

Prompt-based learning, a paradigm that creates a bridge between pre-training and fine-
tuning stages, has proven to be highly effective concerning various NLP tasks, particularly in …

Machine translation often suffers from biased data and algorithms that can lead to
unacceptable errors in system output. While bias in gender norms has been investigated …

Machine translation quality estimation (QE) predicts human judgements of a translation
hypothesis without seeing the reference. State-of-the-art QE systems based on pretrained …

State-of-the-art translation Quality Estimation (QE) models are proven to be biased. More
specifically, they over-rely on monolingual features while ignoring the bilingual semantic …

Abstract Machine translation quality estimation (QE) aims to evaluate the result of translation
without reference. Despite the progress it has made, state-of-the-art QE models are proven …

Fine-grained information on translation errors is helpful for the translation evaluation
community. Existing approaches can not synchronously consider error position and type …