8 citations · 29 across the 14 of their papers we have counts for
14 papers
Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding
Jiali Zeng, Fandong Meng, Yongjing Yin +1
Contemporary translation engines based on the encoder-decoder framework have made significant strides in development. However, the emergence of Large Language Models (LLMs) has dis…
TIM: Teaching Large Language Models to Translate with Comparison
Jiali Zeng, Fandong Meng, Yongjing Yin +1
Open-sourced large language models (LLMs) have demonstrated remarkable efficacy in various tasks with instruction tuning. However, these models can sometimes struggle with tasks th…
Soft Language Clustering for Multilingual Model Pre-training
Jiali Zeng, Yufan Jiang, Yongjing Yin +5
Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language…
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding
Jiali Zeng, Yongjing Yin, Yufan Jiang +2
Contrastive learning has become a new paradigm for unsupervised sentence embeddings. Previous studies focus on instance-wise contrastive learning, attempting to construct positive…
DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition
Jiali Zeng, Yufan Jiang, Yongjing Yin +3
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…
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks
Xinnian Liang, Jing Li, Shuangzhi Wu +4
Unsupervised summarization methods have achieved remarkable results by incorporating representations from pre-trained language models. However, existing methods fail to consider ef…
Task-guided Disentangled Tuning for Pretrained Language Models
Jiali Zeng, Yufan Jiang, Shuangzhi Wu +2
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 result…
Learning Confidence for Transformer-based Neural Machine Translation
Yu Lu, Jiali Zeng, Jiajun Zhang +2
Confidence estimation aims to quantify the confidence of the model prediction, providing an expectation of success. A well-calibrated confidence estimate enables accurate failure p…
Type-Driven Multi-Turn Corrections for Grammatical Error Correction
Shaopeng Lai, Qingyu Zhou, Jiali Zeng +4
Grammatical Error Correction (GEC) aims to automatically detect and correct grammatical errors. In this aspect, dominant models are trained by one-iteration learning while performi…
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings
Shaopeng Lai, Ante Wang, Fandong Meng +6
Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models,…
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension
Siyu Long, Ran Wang, Kun Tao +2
Machine reading comprehension (MRC) is the task that asks a machine to answer questions based on a given context. For Chinese MRC, due to the non-literal and non-compositional sema…
Neural Simile Recognition with Cyclic Multitask Learning and Local Attention
Jiali Zeng, Linfeng Song, Jinsong Su +3
Simile recognition is to detect simile sentences and to extract simile components, i.e., tenors and vehicles. It involves two subtasks: {\it simile sentence classification} and {\i…
Iterative Dual Domain Adaptation for Neural Machine Translation
Jiali Zeng, Yang Liu, Jinsong Su +4
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.…
Graph-based Neural Sentence Ordering
Yongjing Yin, Linfeng Song, Jinsong Su +3
Sentence ordering is to restore the original paragraph from a set of sentences. It involves capturing global dependencies among sentences regardless of their input order. In this p…