Papers by Ziyao Lu
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis (P19-1)
Copied to clipboard
| Challenge: | Experimental results show that our proposed approach yields better attention mechanisms . dominant ASC models are mostly discriminative classifiers based on manual feature engineering . |
| Approach: | They propose a self-supervised approach to aspect-level sentiment classification that mines useful attention supervision information from a training corpus to refine attention mechanisms. |
| Outcome: | The proposed approach yields better attention mechanisms on multiple datasets. |
Multi-Level Cross-Modal Alignment for Speech Relation Extraction (2024.emnlp-main)
Copied to clipboard
Liang Zhang, Zhen Yang, Biao Fu, Ziyao Lu, Liangying Shao, Shiyu Liu, Fandong Meng, Jie Zhou, Xiaoli Wang, Jinsong Su
| Challenge: | Existing studies use synthetic speech to train and evaluate SpeechRE models, hindering their development . modality gap issue limits performance of existing models, limiting future researches . |
| Approach: | They propose to use speech data to train and evaluate SpeechRE models by using real speech . they propose to train a cross-modal alignment model to bridge the modality gap . |
| Outcome: | The proposed model can train to bridge the modality gap between speech encoder and text decoder . the proposed model is based on two real SpeechRE datasets . |
Advancing SMoE for Continuous Domain Adaptation of MLLMs: Adaptive Router and Domain-Specific Loss (2025.acl-long)
Copied to clipboard
| Challenge: | Recent studies have explored Continual Instruction Tuning (CIT) in Multimodal Large Language Models (MLLMs), with a primary focus on Task-incremental CIT, where MLLM are required to continuously acquire new tasks. |
| Approach: | They propose a Sparse Mixture of Expert (SMoE) based method for domain-incremental CIT in Multimodal Large Language Models (MLLMs) . they equip the SMoA module with a domain-specific autoregressive loss (DSAL) they establish a new benchmark to evaluate the efficacy of their method . |
| Outcome: | The proposed method outperforms all baselines and is based on a Sparse Mixture of Experts (SMoE) module . |
BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation (2021.acl-long)
Copied to clipboard
| Challenge: | citing sentences capture salient information in cited papers and the connection between citing and citing papers. |
| Approach: | They propose a BAckground knowledge- and COntent-based framework for citing sentence generation that integrates two types of information: background knowledge and content. |
| Outcome: | The proposed framework outperforms baselines in the citation sentence generation task. |
ArrowGEV: Grounding Events in Video via Learning the Arrow of Time (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches for grounding events in videos are limited by their time-sensitive nature . arrow of time in physics characterizes intrinsic directionality of temporal processes . |
| Approach: | They propose a framework that explicitly models temporal directionality in events to improve event grounding and temporal understanding in VLMs. |
| Outcome: | The proposed framework improves event grounding and directionality understanding in VLMs. |
A Self-Denoising Model for Robust Few-Shot Relation Extraction (2025.acl-long)
Copied to clipboard
| Challenge: | Existing studies assume that the support set contains only accurately labeled instances, but this assumption is often unrealistic. |
| Approach: | They propose a self-denoising model for FSRE which can automatically correct noisy labels of support instances. |
| Outcome: | The proposed model outperforms all baselines on two public datasets showing that it can correct mislabeled support instances. |
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to building cross-lingual summarization systems on dialogue documents are limited. |
| Approach: | They propose a benchmark dataset for building cross-lingual summarization systems on dialogue documents. |
| Outcome: | The proposed model outperforms pipeline models on ClidSum and mDialBART. |
Towards Robust k-Nearest-Neighbor Machine Translation (2022.emnlp-main)
Copied to clipboard
| Challenge: | k-Nearest-Neighbor Machine Translation (kNN-MT) is a popular research paradigm in machine translation. |
| Approach: | They propose a confidence-enhanced kNN-MT model with robust training to reduce noise . they introduce NMT confidence to refine the modeling of important components of kN-MT . |
| Outcome: | The proposed model improves on four benchmark datasets and is robust to training. |