H Li, Y Su, D Cai, Y Wang, L Liu - arXiv preprint arXiv:2202.01110, 2022 - arxiv.org
Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community. Compared with conventional generation models …
R Wang, X Dai - Proceedings of the 60th Annual Meeting of the …, 2022 - aclanthology.org
Abstract Multi-Label Text Classification (MLTC) is a fundamental and challenging task in natural language processing. Previous studies mainly focus on learning text representation …
D Saunders - Journal of Artificial Intelligence Research, 2022 - jair.org
The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time …
Large language models (LLMs) have demonstrated great success in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still …
D Wang, K Fan, B Chen, D Xiong - arXiv preprint arXiv:2204.06175, 2022 - arxiv.org
k-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as a non- parametric solution for domain adaptation in neural machine translation (NMT). It aims to …
F Nie, M Chen, Z Zhang, X Cheng - arXiv preprint arXiv:2212.02216, 2022 - arxiv.org
Pre-trained language models (PLMs) have exhibited remarkable few-shot learning capabilities when provided a few examples in a natural language prompt as demonstrations …
Machine translation models struggle when translating out-of-domain text, which makes domain adaptation a topic of critical importance. However, most domain adaptation methods …
Semi-parametric models, which augment generation with retrieval, have led to impressive results in language modeling and machine translation, due to their ability to retrieve fine …
k-Nearest-Neighbor Machine Translation (kNN-MT) becomes an important research direction of NMT in recent years. Its main idea is to retrieve useful key-value pairs from an …