16 citations · 39 across the 21 of their papers we have counts for
22 papers
Learning to Edit: Aligning LLMs with Knowledge Editing
Yuxin Jiang, Yufei Wang, Chuhan Wu +9
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inp…
MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models
Wai-Chung Kwan, Xingshan Zeng, Yuxin Jiang +6
Large language models (LLMs) are increasingly relied upon for complex multi-turn conversations across diverse real-world applications. However, existing benchmarks predominantly fo…
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models
Yuxin Jiang, Yufei Wang, Xingshan Zeng +7
The ability to follow instructions is crucial for Large Language Models (LLMs) to handle various real-world applications. Existing benchmarks primarily focus on evaluating pure res…
M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models
Wai-Chung Kwan, Xingshan Zeng, Yufei Wang +5
Managing long sequences has become an important and necessary feature for large language models (LLMs). However, it is still an open question of how to comprehensively and systemat…
Evaluating the Efficacy of Length-Controllable Machine Translation
Hao Cheng, Meng Zhang, Weixuan Wang +3
Length-controllable machine translation is a type of constrained translation. It aims to contain the original meaning as much as possible while controlling the length of the transl…
End-to-end Training and Decoding for Pivot-based Cascaded Translation Model
Hao Cheng, Meng Zhang, Liangyou Li +2
Utilizing pivot language effectively can significantly improve low-resource machine translation. Usually, the two translation models, source-pivot and pivot-target, are trained ind…
AdaTranS: Adapting with Boundary-based Shrinking for End-to-End Speech Translation
Xingshan Zeng, Liangyou Li, Qun Liu
To alleviate the data scarcity problem in End-to-end speech translation (ST), pre-training on data for speech recognition and machine translation is considered as an important tech…
SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme
Yusen Sun, Liangyou Li, Qun Liu +1
Although lyrics generation has achieved significant progress in recent years, it has limited practical applications because the generated lyrics cannot be performed without composi…
FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models
Yinpeng Guo, Liangyou Li, Xin Jiang +1
Cross-lingual transfer (CLT) is of various applications. However, labeled cross-lingual corpus is expensive or even inaccessible, especially in the fields where labels are private,…
Triangular Transfer: Freezing the Pivot for Triangular Machine Translation
Meng Zhang, Liangyou Li, Qun Liu
Triangular machine translation is a special case of low-resource machine translation where the language pair of interest has limited parallel data, but both languages have abundant…
Universal Conditional Masked Language Pre-training for Neural Machine Translation
Pengfei Li, Liangyou Li, Meng Zhang +2
Pre-trained sequence-to-sequence models have significantly improved Neural Machine Translation (NMT). Different from prior works where pre-trained models usually adopt an unidirect…
Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training
Minghao Wu, Yitong Li, Meng Zhang +3
Learning multilingual and multi-domain translation model is challenging as the heterogeneous and imbalanced data make the model converge inconsistently over different corpora in re…
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation
Minghan Wang, Yuxia Wang, Chang Su +9
This paper describes our work in participation of the IWSLT-2021 offline speech translation task. Our system was built in a cascade form, including a speaker diarization module, an…
Multilingual Speech Translation with Unified Transformer: Huawei Noah's Ark Lab at IWSLT 2021
Xingshan Zeng, Liangyou Li, Qun Liu
This paper describes the system submitted to the IWSLT 2021 Multilingual Speech Translation (MultiST) task from Huawei Noah's Ark Lab. We use a unified transformer architecture for…
Learning Multilingual Representation for Natural Language Understanding with Enhanced Cross-Lingual Supervision
Yinpeng Guo, Liangyou Li, Xin Jiang +1
Recently, pre-training multilingual language models has shown great potential in learning multilingual representation, a crucial topic of natural language processing. Prior works g…
RealTranS: End-to-End Simultaneous Speech Translation with Convolutional Weighted-Shrinking Transformer
Xingshan Zeng, Liangyou Li, Qun Liu
End-to-end simultaneous speech translation (SST), which directly translates speech in one language into text in another language in real-time, is useful in many scenarios but has n…
An Approach to Improve Robustness of NLP Systems against ASR Errors
Tong Cui, Jinghui Xiao, Liangyou Li +2
Speech-enabled systems typically first convert audio to text through an automatic speech recognition (ASR) model and then feed the text to downstream natural language processing (N…
Dependency Graph-to-String Statistical Machine Translation
Liangyou Li, Andy Way, Qun Liu
We present graph-based translation models which translate source graphs into target strings. Source graphs are constructed from dependency trees with extra links so that non-syntac…
Future-Guided Incremental Transformer for Simultaneous Translation
Shaolei Zhang, Yang Feng, Liangyou Li
Simultaneous translation (ST) starts translations synchronously while reading source sentences, and is used in many online scenarios. The previous wait-k policy is concise and achi…
Document Graph for Neural Machine Translation
Mingzhou Xu, Liangyou Li, Derek. F. Wong +2
Previous works have shown that contextual information can improve the performance of neural machine translation (NMT). However, most existing document-level NMT methods only consid…
Pretrained Language Models for Document-Level Neural Machine Translation
Liangyou Li, Xin Jiang, Qun Liu
Previous work on document-level NMT usually focuses on limited contexts because of degraded performance on larger contexts. In this paper, we investigate on using large contexts wi…
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation
Yun Chen, Liangyou Li, Xin Jiang +2
Despite the success of neural machine translation (NMT), simultaneous neural machine translation (SNMT), the task of translating in real time before a full sentence has been observ…