Research on prompting has shown excellent performance with little or even no supervised
training across many tasks. However, prompting for machine translation is still under …

Recent machine translation (MT) metrics calibrate their effectiveness by correlating with
human judgment. However, these results are often obtained by averaging predictions across …

The aim of the study is to compare two different approaches to machine translation—
statistical and neural—using automatic MT metrics of error rate and residuals. We examined …

We propose a genetic algorithm (GA) based method for modifying n-best lists produced by a
machine translation (MT) system. Our method offers an innovative approach to improving MT …

Abstract The Metric Score Landscape Challenge (MSLC23) dataset aims to gain insight into
metric scores on a broader/wider landscape of machine translation (MT) quality. It provides a …

We benchmark the performance of segmentlevel metrics submitted to WMT 2023 using the
ACES Challenge Set (Amrhein et al., 2022). The challenge set consists of 36K examples …

Building a reliable visual question answering (VQA) system across different languages is a
challenging problem, primarily due to the lack of abundant samples for training. To address …

Large language models (LLMs) have demonstrated impressive capabilities in natural
language generation. However, their output quality can be inconsistent, posing challenges …

We introduce a dataset comprising commercial machine translations, gathered weekly over
six years across 12 translation directions. Since human A/B testing is commonly used, we …

The translation of low-resource languages remains a significant challenge in Natural
Language Processing (NLP) due to the scarcity of high-quality parallel data for training …