no code implementations • WMT (EMNLP) 2021 • Longyue Wang, Mu Li, Fangxu Liu, Shuming Shi, Zhaopeng Tu, Xing Wang, Shuangzhi Wu, Jiali Zeng, Wen Zhang
Based on our success in the last WMT, we continuously employed advanced techniques such as large batch training, data selection and data filtering.
1 code implementation • EMNLP 2021 • Jiali Zeng, Shuangzhi Wu, Yongjing Yin, Yufan Jiang, Mu Li
Across an extensive set of experiments on 10 machine translation tasks, we find that RAN models are competitive and outperform their Transformer counterpart in certain scenarios, with fewer parameters and inference time.
no code implementations • 16 Jan 2024 • Xinwei Long, Jiali Zeng, Fandong Meng, Zhiyuan Ma, Kaiyan Zhang, BoWen Zhou, Jie zhou
Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications.
no code implementations • 6 Nov 2023 • Jiali Zeng, Fandong Meng, Yongjing Yin, Jie zhou
Contemporary translation engines built upon the encoder-decoder framework have reached a high level of development, while the emergence of Large Language Models (LLMs) has disrupted their position by offering the potential for achieving superior translation quality.
1 code implementation • 10 Jul 2023 • Jiali Zeng, Fandong Meng, Yongjing Yin, Jie zhou
Open-sourced large language models (LLMs) have demonstrated remarkable efficacy in various tasks with instruction tuning.
no code implementations • 13 Jun 2023 • Jiali Zeng, Yufan Jiang, Yongjing Yin, Yi Jing, Fandong Meng, Binghuai Lin, Yunbo Cao, Jie zhou
Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is limited in size.
no code implementations • 15 Nov 2022 • Jiali Zeng, Yufan Jiang, Yongjing Yin, Xu Wang, Binghuai Lin, Yunbo Cao
We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER).
no code implementations • 7 Nov 2022 • Jiali Zeng, Yongjing Yin, Yufan Jiang, Shuangzhi Wu, Yunbo Cao
Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts.
1 code implementation • COLING 2022 • Xinnian Liang, Jing Li, Shuangzhi Wu, Jiali Zeng, Yufan Jiang, Mu Li, Zhoujun Li
To tackle this problem, in this paper, we proposed an efficient Coarse-to-Fine Facet-Aware Ranking (C2F-FAR) framework for unsupervised long document summarization, which is based on the semantic block.
1 code implementation • Findings (ACL) 2022 • Jiali Zeng, Yufan Jiang, Shuangzhi Wu, Yongjing Yin, Mu Li
Pretrained language models (PLMs) trained on large-scale unlabeled corpus are typically fine-tuned on task-specific downstream datasets, which have produced state-of-the-art results on various NLP tasks.
1 code implementation • ACL 2022 • Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, Mu Li
Confidence estimation aims to quantify the confidence of the model prediction, providing an expectation of success.
1 code implementation • Findings (ACL) 2022 • Shaopeng Lai, Qingyu Zhou, Jiali Zeng, Zhongli Li, Chao Li, Yunbo Cao, Jinsong Su
First, they simply mix additionally-constructed training instances and original ones to train models, which fails to help models be explicitly aware of the procedure of gradual corrections.
1 code implementation • EMNLP 2021 • Shaopeng Lai, Ante Wang, Fandong Meng, Jie zhou, Yubin Ge, Jiali Zeng, Junfeng Yao, Degen Huang, Jinsong Su
Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models.
no code implementations • ACL 2021 • Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, Mu Li
Attention mechanisms have achieved substantial improvements in neural machine translation by dynamically selecting relevant inputs for different predictions.
1 code implementation • COLING 2020 • Siyu Long, Ran Wang, Kun Tao, Jiali Zeng, Xin-yu Dai
Machine reading comprehension (MRC) is the task that asks a machine to answer questions based on a given context.
1 code implementation • 19 Dec 2019 • Jiali Zeng, Linfeng Song, Jinsong Su, Jun Xie, Wei Song, Jiebo Luo
Simile recognition is to detect simile sentences and to extract simile components, i. e., tenors and vehicles.
no code implementations • IJCNLP 2019 • Jiali Zeng, Yang Liu, Jinsong Su, Yubin Ge, Yaojie Lu, Yongjing Yin, Jiebo Luo
Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model.
1 code implementation • 16 Dec 2019 • Yongjing Yin, Linfeng Song, Jinsong Su, Jiali Zeng, Chulun Zhou, Jiebo Luo
Sentence ordering is to restore the original paragraph from a set of sentences.
1 code implementation • EMNLP 2018 • Jiali Zeng, Jinsong Su, Huating Wen, Yang Liu, Jun Xie, Yongjing Yin, Jianqiang Zhao
Based on this intuition, in this paper, we devote to distinguishing and exploiting word-level domain contexts for multi-domain NMT.