The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

Natural language processing (NLP) is a subfield of artificial intelligence that focuses on
enabling computers to understand and process human languages. In the last five years, we …

Natural language generation has witnessed significant advancements due to the training of
large language models on vast internet-scale datasets. Despite these advancements, there …

In this paper, we present a conceptually simple while empirically powerful framework for
abstractive summarization, SimCLS, which can bridge the gap between the learning …

Large-scale language models often learn behaviors that are misaligned with user
expectations. Generated text may contain offensive or toxic language, contain significant …

Neural text generation is a key tool in natural language applications, but it is well known
there are major problems at its core. In particular, standard likelihood training and decoding …

We present a survey covering the state of the art in low-resource machine translation (MT)
research. There are currently around 7,000 languages spoken in the world and almost all …

Amid the rapid advancement of neural machine translation, the challenge of data sparsity
has been a major obstacle. To address this issue, this study proposes a general data …

Single document summarization is the task of producing a shorter version of a document
while preserving its principal information content. In this paper we conceptualize extractive …

This paper presents the results of the WMT17 shared tasks, which included three machine
translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and …