Papers by Long Ye
ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching (2026.findings-acl)
Copied to clipboard
Han Zhu, Wei Kang, Liyong Guo, Zengwei Yao, Fangjun Kuang, Weiji Zhuang, Zhaoqing Li, Zhifeng Han, Dong Zhang, Xin Zhang, Xingchen Song, Lingxuan Ye, Long Lin, Daniel Povey
| Challenge: | Existing autoregressive models for dialogue generation suffer from high latency and stability issues. |
| Approach: | They propose a non-autoregressive (NAR) zero-shot spoken dialogue generation model based on flow-matching. |
| Outcome: | The proposed model outperforms existing models in speech generation due to poor speech intelligibility and turn-taking precision. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
Copied to clipboard
Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen
| Challenge: | Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed . |
| Approach: | They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities. |
| Outcome: | The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech . |
CogEmp:A Cognitive Empathy-Oriented Dialogue System for Structured Psychological Counseling (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing models lack accurate modeling of cognitive empathy, especially the ability to understand users’ emotions and their underlying psychological causes. |
| Approach: | They propose a model tailored for the Chinese cultural context that integrates cognitive empathy into LLMs. |
| Outcome: | The proposed model outperforms existing models in key evaluation metrics, particularly in empathy, comprehensibility, and professionalism. |
NeuroSym-Cal: Bridging the Reasoning-Execution Gap in Code Generation via Hierarchical Calibration (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing calibration methods rely on the assumption that consensus implies correctness . Existing methods fail under systematic errors, leading to miscalibrated high-confidence predictions. |
| Approach: | They propose a hierarchical calibration framework that measures confidence at two levels . they propose sensitivity analysis to measure local curvature of deductive process . |
| Outcome: | The proposed framework de-saturates overconfident errors and improves selective generation performance on OOD benchmarks. |
Multi-Hop Transformer for Document-Level Machine Translation (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to document-level neural machine translation (NMT) simply introduce the representations of context sentences without explicitly characterizing the inter-sentence reasoning process. |
| Approach: | They propose a novel multi-hop Transformer which explicitly models the human-like draft-editing and reasoning process by attending to multiple antecedent sentences iteratively. |
| Outcome: | Experiments on four widely used document translation tasks show that the proposed model significantly improves document-level translation performance and tackles discourse phenomena such as coreference error and the problem of polysemy. |
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data (P19-1)
Copied to clipboard
| Challenge: | Existing methods to perform relation extraction are feature-based or kernel-based, but the results of our study show that they can improve the performance of a baseline model with more than 10% absolute increase in F1-score. |
| Approach: | They propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. |
| Outcome: | The proposed model outperforms the state-of-the-art models on ACE 2005 Chinese and English corpus and significantly improves the performance of a baseline model with more than 10% increase in F1-score. |
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models have been criticized for lack of integration of bilingual lexical information into the neural architecture. |
| Approach: | They propose a neural architecture to incorporate bilingual dictionaries into Neural Machine Translation models by introducing three new components: Pointer, Disambiguator, and Copier. |
| Outcome: | The proposed method achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionary can potentially be used; (2) Disambiguator synthesizes contextual information from source view and target view, both of which contribute to distinguishing translation of a specific source word from multiple candidates in dicaries; (3) Copier systematically connects Pointer and Disambiguators based on a hierarchical |