11 citations · 14 across the 6 of their papers we have counts for
6 papers
Large Language Model Should Understand Pinyin for Chinese ASR Error Correction
Yuang Li, Xiaosong Qiao, Xiaofeng Zhao +4
Large language models can enhance automatic speech recognition systems through generative error correction. In this paper, we propose Pinyin-enhanced GEC, which leverages Pinyi, th…
To Recommend or Not: Recommendability Identification in Conversations with Pre-trained Language Models
Zhefan Wang, Weizhi Ma, Min Zhang
Most current recommender systems primarily focus on what to recommend, assuming users always require personalized recommendations. However, with the widely spread of ChatGPT and ot…
SiLLM: Large Language Models for Simultaneous Machine Translation
Shoutao Guo, Shaolei Zhang, Zhengrui Ma +2
Simultaneous Machine Translation (SiMT) generates translations while reading the source sentence, necessitating a policy to determine the optimal timing for reading and generating…
Unsupervised Sign Language Translation and Generation
Zhengsheng Guo, Zhiwei He, Wenxiang Jiao +6
Motivated by the success of unsupervised neural machine translation (UNMT), we introduce an unsupervised sign language translation and generation network (USLNet), which learns fro…
G-SPEED: General SParse Efficient Editing MoDel
Haoke Zhang, Yue Wang, Juntao Li +2
Large Language Models~(LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages. Through human-model interactions, LLMs can automati…
Translating Phrases in Neural Machine Translation
Xing Wang, Zhaopeng Tu, Deyi Xiong +1
Phrases play an important role in natural language understanding and machine translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is difficult to integrate them…