Reinforcement learning from human feedback (RLHF) can improve the quality of large
language model's (LLM) outputs by aligning them with human preferences. We propose a …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

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 …

Beam search is the go-to method for decoding auto-regressive machine translation models.
While it yields consistent improvements in terms of BLEU, it is only concerned with finding …

Natural language processing researchers have identified limitations of evaluation
methodology for generation tasks, with new questions raised about the validity of automatic …

Context-aware neural machine translation (NMT) incorporates contextual information of
surrounding texts, that can improve the translation quality of document-level machine …

Chatbots or conversational systems are used in various sectors such as banking,
healthcare, e-commerce, customer support, etc. These chatbots are mainly available for …

We propose a new architecture for adapting a sentence-level sequence-to-sequence
transformer by incorporating multiple pretrained document context signals and assess the …

The widely available data is exploding with the continuous development of the Internet,
leading people to the era of big data. Nowadays, massive amounts of English information …

Noisy channel models have been especially effective in neural machine translation (NMT).
However, recent approaches like" beam search and rerank"(BSR) incur significant …