8 citations · 27 across the 17 of their papers we have counts for
18 papers
Anthropogenic Regional Adaptation in Multimodal Vision-Language Model
Samuel Cahyawijaya, Peerat Limkonchotiwat, Tack Hwa Wong +45
While the field of vision-language (VL) has achieved remarkable success in integrating visual and textual information across multiple languages and domains, there is still no dedic…
Resurfacing Paralinguistic Awareness in Large Audio Language Models
Hao Yang, Minghan Wang, Tongtong Wu +3
Large Audio Language Models (LALMs) have expanded the interaction with human to speech modality, which introduces great interactive potential, due to the paralinguistic cues implic…
Discourse Graph Guided Document Translation with Large Language Models
Viet-Thanh Pham, Minghan Wang, Hao-Han Liao +1
Adapting large language models to full document translation remains challenging due to the difficulty of capturing long-range dependencies and preserving discourse coherence throug…
Towards Inference-time Scaling for Continuous Space Reasoning
Minghan Wang, Thuy-Trang Vu, Ehsan Shareghi +1
Inference-time scaling through multiple sample generation in combination with Process- or Outcome-Reward Model (PRM or ORM) re-ranking has proven effective for text-based reasoning…
Discrete Minds in a Continuous World: Do Language Models Know Time Passes?
Minghan Wang, Ye Bai, Thuy-Trang Vu +2
While Large Language Models (LLMs) excel at temporal reasoning tasks like event ordering and duration estimation, their ability to perceive the actual passage of time remains unexp…
MAPLE: Multi-Agent Adaptive Planning with Long-Term Memory for Table Reasoning
Ye Bai, Minghan Wang, Thuy-Trang Vu
Table-based question answering requires complex reasoning capabilities that current LLMs struggle to achieve with single-pass inference. Existing approaches, such as Chain-of-Thoug…
SpeechDialogueFactory: Generating High-Quality Speech Dialogue Data to Accelerate Your Speech-LLM Development
Minghan Wang, Ye Bai, Yuxia Wang +3
High-quality speech dialogue datasets are crucial for Speech-LLM development, yet existing acquisition methods face significant limitations. Human recordings incur high costs and p…
Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model
Minghan Wang, Viet-Thanh Pham, Farhad Moghimifar +1
Despite achieving remarkable performance, machine translation (MT) research remains underexplored in terms of translating cultural elements in languages, such as idioms, proverbs,…
Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models
Minghan Wang, Thuy-Trang Vu, Yuxia Wang +2
Simultaneous machine translation (SimulMT) presents a challenging trade-off between translation quality and latency. Recent studies have shown that LLMs can achieve good performanc…
UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction
Jiaxin Guo, Minghan Wang, Xiaosong Qiao +9
Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER). Previous works u…
INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion
Hengchao Shang, Zongyao Li, Daimeng Wei +5
Computer-aided translation (CAT) aims to enhance human translation efficiency and is still important in scenarios where machine translation cannot meet quality requirements. One fu…
Simultaneous Machine Translation with Large Language Models
Minghan Wang, Jinming Zhao, Thuy-Trang Vu +3
Real-world simultaneous machine translation (SimulMT) systems face more challenges than just the quality-latency trade-off. They also need to address issues related to robustness w…
Knowledge-Prompted Estimator: A Novel Approach to Explainable Machine Translation Assessment
Hao Yang, Min Zhang, Shimin Tao +3
Cross-lingual Machine Translation (MT) quality estimation plays a crucial role in evaluating translation performance. GEMBA, the first MT quality assessment metric based on Large L…
Text Style Transfer Back-Translation
Daimeng Wei, Zhanglin Wu, Hengchao Shang +6
Back Translation (BT) is widely used in the field of machine translation, as it has been proved effective for enhancing translation quality. However, BT mainly improves the transla…
Diformer: Directional Transformer for Neural Machine Translation
Minghan Wang, Jiaxin Guo, Yuxia Wang +8
Autoregressive (AR) and Non-autoregressive (NAR) models have their own superiority on the performance and latency, combining them into one model may take advantage of both. Current…
Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models
Zhengzhe Yu, Jiaxin Guo, Minghan Wang +11
Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but it reaches the upper bound of translation quality when the number of encod…
Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation
Jiaxin Guo, Minghan Wang, Daimeng Wei +11
Recently, non-autoregressive (NAT) models predict outputs in parallel, achieving substantial improvements in generation speed compared to autoregressive (AT) models. While performi…
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…