Automatically evaluating the quality of language generation is critical. Although recent
learned metrics show high correlation with human judgement, these metrics can not explain …

Large language models (LLMs) have demonstrated remarkable potential in handling
multilingual machine translation (MMT). In this paper, we systematically investigate the …

The state-of-the-art language model-based automatic metrics, eg BARTScore, benefiting
from large-scale contextualized pre-training, have been successfully used in a wide range of …

Establishing an automatic evaluation metric that closely aligns with human judgments is
essential for effectively developing image captioning models. Recent data-driven metrics …

Is it possible to train a general metric for evaluating text generation quality without human
annotated ratings? Existing learned metrics either perform unsatisfactorily across text …

We propose" Conceptual Coverage Across Languages"(CoCo-CroLa), a technique for
benchmarking the degree to which any generative text-to-image system provides …

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

Generative large language models (LLMs), eg, ChatGPT, have demonstrated remarkable
proficiency across several NLP tasks, such as machine translation, text summarization …

We present a proof of concept investigating whether native language identification and fact
checking information improves a language model (GPT-2) classifier which determines …

One of the core goals of Natural Language Processing (NLP) is to develop computational
representations and methods to compare and contrast text meaning across languages. Such …