Papers by Jiaze Chen
An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to generate event roles require a given generation order . parallel methods suffer from inadequate training and manifest zero accuracies on some event roles. |
| Approach: | They propose an iteratively parallel generation method with the Pre-Filling strategy to generate event roles in parallel to avoid order selection. |
| Outcome: | The proposed method outperforms other entity-enhanced models and achieves state-of-the-art performance on two public datasets. |
On Tree-Based Neural Sentence Modeling (D18-1)
Copied to clipboard
| Challenge: | Existing tree-based sentence modeling approaches adopt syntactic parsing trees as the explicit structure prior. |
| Approach: | They replace parsing trees with trivial trees to study their effectiveness . they found that tree-based sentence modeling gives better results when crucial words are closer to the final representation . |
| Outcome: | The proposed tree-based sentences have shown better results on many downstream tasks. |
Xiaomingbot: A Multilingual Robot News Reporter (2020.acl-demos)
Copied to clipboard
Runxin Xu, Jun Cao, Mingxuan Wang, Jiaze Chen, Hao Zhou, Ying Zeng, Yuping Wang, Li Chen, Xiang Yin, Xijin Zhang, Songcheng Jiang, Yuxuan Wang, Lei Li
| Challenge: | Xiaomingbot is a multilingual and multimodal software robot with four capabilities: news generation, news translation, news reading and avatar animation. |
| Approach: | They propose to build a multilingual and multimodal software robot with four inte- gal capabilities: news generation, news translation, news reading and avatar animation. |
| Outcome: | The proposed system generates Chinese news, then reads it in multiple languages and generates an animated avatar reading it. |
MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Using MTG, we train and evaluate multilingual text generation models using human-annotated data. |
| Approach: | They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages. |
| Outcome: | The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios. |
Contrastive Aligned Joint Learning for Multilingual Summarization (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing summarization systems for multilingual text summarizing are limited due to the lack of large-scale data in multiple languages. |
| Approach: | They propose a multilingual summarization system that can understand documents in multiple languages and generate summaries in the corresponding language. |
| Outcome: | The proposed model improves over monolingual models in all languages and transferable to other languages. |
Multilingual Generation in Abstractive Summarization: A Comparative Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity. |
| Approach: | They propose to classify multilingual generation methodologies into three categories based on their underlying modeling principles . they introduce an automatic metric to mitigate spurious correlations associated with language mixing . |
| Outcome: | The proposed model improves in high-resource, low-resourced, and zero-shot scenarios. |
SWE-Swiss: A Multi-Task Fine-Tuning and RL Recipe for High-Performance Issue Resolution (2026.findings-acl)
Copied to clipboard
Zhenyu He, Qingping Yang, Wei Shen, Xiaojian Zhong, Kechi Zhang, Chenxin An, Wenlei Shi, Tianle Cai, Di He, Jiaze Chen, Jingjing Xu
| Challenge: | SWE-Swiss-32B demonstrates strong generalization to other common LLM benchmarks. |
| Approach: | They propose a two-phase training recipe that decomposes issue resolution into three core skills: Localization, Repair, and Unit Test Generation. |
| Outcome: | The proposed model achieves a 60.2% score on the SWE-bench Verified benchmark and is in the top-tier performance bracket of much larger models. |
SciVerse: Unveiling the Knowledge Comprehension and Visual Reasoning of LMMs on Multi-modal Scientific Problems (2025.findings-acl)
Copied to clipboard
| Challenge: | SciVerse is a multi-modal scientific evaluation benchmark to assess large multi-models . it examines the scientific knowledge comprehension, multi-mod content interpretation and Chain-of-Thought reasoning . authors examine the scientific proficiency of LMMs in scientific domains based on their work . |
| Approach: | They propose a multi-modal scientific evaluation benchmark to thoroughly assess Large Multi-modal Models across 5,735 test instances in five different versions. |
| Outcome: | The proposed evaluation reveals critical limitations in LMMs' scientific proficiency and provides new insights into future developments. |
Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering progress in this area. |
| Approach: | They propose a new ASTE dataset that is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews. |
| Outcome: | The proposed dataset is manually annotated to better fit real-world scenarios. |
Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs. |
| Approach: | They propose a novel approach for joint answer prediction and proof generation using an induced graphical model. |
| Outcome: | The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions. |
Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Abstractive summarization models often produce inconsistent statements or false facts. |
| Approach: | They propose an efficient weak-supervised adversarial data augmentation approach to generate factual consistency datasets by backpropagating gradients on token embeddings. |
| Outcome: | The proposed model can make interpretable factual errors tracing on public datasets and is cost-effective. |