29 citations · 104 across the 17 of their papers we have counts for
17 papers
Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs
Longyue Wang, Zhaopeng Tu, Yan Gu +14
Translating literary works has perennially stood as an elusive dream in machine translation (MT), a journey steeped in intricate challenges. To foster progress in this domain, we h…
GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation
Zhanyu Wang, Longyue Wang, Zhen Zhao +7
While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of in…
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering
Yunxin Li, Longyue Wang, Baotian Hu +5
The emergence of multimodal large models (MLMs) has significantly advanced the field of visual understanding, offering remarkable capabilities in the realm of visual question answe…
On the Cultural Gap in Text-to-Image Generation
Bingshuai Liu, Longyue Wang, Chenyang Lyu +4
One challenge in text-to-image (T2I) generation is the inadvertent reflection of culture gaps present in the training data, which signifies the disparity in generated image quality…
Revisiting Non-Autoregressive Translation at Scale
Zhihao Wang, Longyue Wang, Jinsong Su +2
In real-world systems, scaling has been critical for improving the translation quality in autoregressive translation (AT), which however has not been well studied for non-autoregre…
TaleCrafter: Interactive Story Visualization with Multiple Characters
Yuan Gong, Youxin Pang, Xiaodong Cun +8
Accurate Story visualization requires several necessary elements, such as identity consistency across frames, the alignment between plain text and visual content, and a reasonable…
MAGE: Machine-generated Text Detection in the Wild
Yafu Li, Qintong Li, Leyang Cui +6
Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective AI-generated text detection to mitigate risks like the spread of fake new…
Prompt-Learning for Cross-Lingual Relation Extraction
Chiaming Hsu, Changtong Zan, Liang Ding +5
Relation Extraction (RE) is a crucial task in Information Extraction, which entails predicting relationships between entities within a given sentence. However, extending pre-traine…
Document-Level Machine Translation with Large Language Models
Longyue Wang, Chenyang Lyu, Tianbo Ji +4
Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks. Taking document-level…
Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production
Ante Wang, Linfeng Song, Qi Liu +5
Knowledge-aided dialogue response generation aims at augmenting chatbots with relevant external knowledge in the hope of generating more informative responses. The majority of prev…
ngram-OAXE: Phrase-Based Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation
Cunxiao Du, Zhaopeng Tu, Longyue Wang +1
Recently, a new training oaxe loss has proven effective to ameliorate the effect of multimodality for non-autoregressive translation (NAT), which removes the penalty of word order…
On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation
Xuebo Liu, Longyue Wang, Derek F. Wong +4
Pre-training (PT) and back-translation (BT) are two simple and powerful methods to utilize monolingual data for improving the model performance of neural machine translation (NMT).…
On the Copying Behaviors of Pre-Training for Neural Machine Translation
Xuebo Liu, Longyue Wang, Derek F. Wong +4
Previous studies have shown that initializing neural machine translation (NMT) models with the pre-trained language models (LM) can speed up the model training and boost the model…
Progressive Multi-Granularity Training for Non-Autoregressive Translation
Liang Ding, Longyue Wang, Xuebo Liu +3
Non-autoregressive translation (NAT) significantly accelerates the inference process via predicting the entire target sequence. However, recent studies show that NAT is weak at lea…
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation
Liang Ding, Longyue Wang, Xuebo Liu +3
Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation (NAT) models. However, there exists a discrepancy on low-freque…
TranSmart: A Practical Interactive Machine Translation System
Guoping Huang, Lemao Liu, Xing Wang +5
Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed. This technique report introduces TranSmart, a practical human-machine…
Modeling Recurrence for Transformer
Jie Hao, Xing Wang, Baosong Yang +3
Recently, the Transformer model that is based solely on attention mechanisms, has advanced the state-of-the-art on various machine translation tasks. However, recent studies reveal…