Papers by Yongqi Luo
Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches to generative language models struggle to handle the increasing complexity of multi-turn Text-to-SQL tasks. |
| Approach: | They propose a framework which enhances generative language models with dual-extractive modules designed to track schema and contextual changes in multi-turn Text-to-SQL. |
| Outcome: | The proposed framework achieves state-of-the-art performance on SparC and CoSQL datasets and significantly improves execution accuracy in multi-turn interactions by 7.1% and 9.55%. |
CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing generative ASQP approaches do not model the contextual relationship of the review sentence to predict implicit terms. |
| Approach: | They propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network to enhance alignment of aspects and opinions. |
| Outcome: | The proposed framework improves the alignment of aspects and opinions, whether explicit or implicit, and improves on three benchmark datasets. |
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)
Copied to clipboard
Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu
| Challenge: | Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT . |
| Approach: | They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models. |
| Outcome: | The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache. |
S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis (2024.acl-long)
Copied to clipboard
| Challenge: | Existing graph-based approaches to learn static structures and dynamic latent trees are lacking in incorporating semantic and syntactic information simultaneously within complex global structures. |
| Approach: | They propose a graph-based framework that incorporates semantic and syntactic information simultaneously within global structures. |
| Outcome: | The proposed framework removes irrelevant contexts and syntactic dependencies and achieves complementarity across diverse structures. |