Transformer-based language models (TLMs) have widely been recognized to be a cutting-
edge technology for the successful development of deep-learning-based solutions to …

Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

With direct access to human-written reference as memory, retrieval-augmented generation
has achieved much progress in a wide range of text generation tasks. Since better memory …

This paper presents the results of the news translation task, the multilingual low-resource
translation for Indo-European languages, the triangular translation task, and the automatic …

We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the
inclusion of pre-specified lexical constraints. The algorithm can be used with any model that …

Neural sequence generation models are known to “hallucinate”, by producing outputs that
are unrelated to the source text. These hallucinations are potentially harmful, yet it remains …

This paper describes the creation process and statistics of the official United Nations Parallel
Corpus, the first parallel corpus composed from United Nations documents published by the …

Neural Machine Translation (NMT) has obtained state-of-the art performance for several
language pairs, while only using parallel data for training. Target-side monolingual data …

We present the 1.0 release of our paraphrase database, PPDB. Its English portion, PPDB:
Eng, contains over 220 million paraphrase pairs, consisting of 73 million phrasal and 8 …

Language identification (" LI") is the problem of determining the natural language that a
document or part thereof is written in. Automatic LI has been extensively researched for over …