Papers by Xinyuan Zhang
Scalable Data Synthesis through Human-like Cognitive Imitation and Data Recombination (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) rely on massive amounts of training data, however, the quantity of empirically observed data is limited. |
| Approach: | They propose a data synthesis framework that mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources. |
| Outcome: | The proposed framework mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources thereby enhancing advanced reasoning capabilities in large language models. |
AffectCodec: Emotion-Preserving Neural Speech Codec for Expressive Speech Modeling (2026.findings-acl)
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| Challenge: | Existing codecs optimize acoustic reconstruction, leaving emotion expressiveness insufficiently modeled at the representation level. |
| Approach: | They propose an emotion-guided neural speech codec that preserves emotional information while maintaining semantic fidelity and prosodic naturalness. |
| Outcome: | The proposed codec preserves emotional cues while maintaining semantic fidelity and prosodic naturalness. |
Fast and Accurate Factual Inconsistency Detection Over Long Documents (2023.emnlp-main)
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| Challenge: | Generative AI models exhibit remarkable potential, however, hallucinations across various tasks present a significant challenge, particularly for longer inputs. |
| Approach: | They propose a task-agnostic model that uses large text chunks to condition over long texts and employ a novel algorithm to explain its decisions through relevant source sentence retrieval. |
| Outcome: | The proposed model outperforms existing methods on benchmarks and a new long-form dialogue dataset and surpasses competitive systems in efficiency and model explanation evaluations. |
Joint Embedding of Words and Labels for Text Classification (P18-1)
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Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
| Challenge: | Existing approaches to text classification use word embeddings to capture semantic regularities between words. |
| Approach: | They propose to view text classification as a label-word joint embedding problem . they use a framework that measures compatibility between text sequences and labels . |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on large text datasets. |
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment (D18-1)
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| Challenge: | Existing approaches to network embeddings focus on one-hot representations of vertices, which are not able to capture relationships between verti- ces. |
| Approach: | They propose to integrate semantic features into network embeddings by matching important words between text sequences for all pairs of vertices. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding methods on three real-world benchmarks for downstream tasks including link prediction and multi-label vertex classification. |
Syntax-Infused Variational Autoencoder for Text Generation (P19-1)
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| Challenge: | Experimental results demonstrate the generative superiority of SIVAE on both reconstruction and targeted syntactic evaluations. |
| Approach: | They propose a syntax-infused variational autoencoder that integrates sentences with their syntactic trees to improve the grammar of generated sentences. |
| Outcome: | The proposed model improves the grammar of generated sentences by integrating sentences with syntactic trees. |
Memory-QA: Answering Recall Questions Based on Multimodal Memories (2025.emnlp-main)
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Hongda Jiang, Xinyuan Zhang, Siddhant Garg, Rishab Arora, Shiun-Zu Kuo, Jiayang Xu, Aaron Colak, Xin Luna Dong
| Challenge: | Memory-QA is a real-world task that involves answering recall questions about visual content from previously stored multimodal memories. |
| Approach: | They propose a memory-QA task that involves answering recall questions about visual content from previously stored multimodal memories. |
| Outcome: | The proposed solution improves memory recording, compression, storage, and search accuracy over state-of-the-art solutions. |
Semantic Matching for Sequence-to-Sequence Learning (2020.findings-emnlp)
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| Challenge: | Existing models for sequence-to-sequence semantic matching do not match target sentences . Optimal partial transport (OPT) is a technique that partially matches semantically meaningful words between source and partial target sequences. |
| Approach: | They propose a semantic matching scheme based on the Optimal Partial Transport (OPT) to match semantically meaningful words between source and partial target sequences. |
| Outcome: | Extensive experiments show that the proposed approach improves over sequence-to-sequence models. |
SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types (2025.findings-acl)
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Xuanliang Zhang, Dingzirui Wang, Baoxin Wang, Longxu Dou, Xinyuan Lu, Keyan Xu, Dayong Wu, Qingfu Zhu
| Challenge: | Existing scientific question answering datasets lack diverse reasoning types and neglect relevance between tables and text. |
| Approach: | They propose a scientific question answering benchmark for scientific tables and text with diverse reasoning types (SCITAT) to address these challenges, they propose QA benchmark which incorporates tables and texts to ensure that the questions encompass both tables and textes. |
| Outcome: | The proposed benchmark improves by 4.1% over baselines on SCITAT. |
Dash-M5H: An Interactive Dashboard for Multi-Modal, Multi-Model Mental Health Assessment (2026.acl-demo)
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Raymond Alavo, Xinyuan Zhang, Gemza Ademaj, Junhui Cai, Hyeokhyen Kwon, Robert Cotes, Gari D. Clifford, Ahmed Abbasi
| Challenge: | Dash-M5H integrates transcript text, audio, and facial behavior with a clinically grounded VLM prediction pipeline that produces DSM-5-aligned depression predictions. |
| Approach: | They propose a dashboard that integrates multimodal behavioral data with multi-model signal outputs of recorded clinical interviews. |
| Outcome: | Dash-M5H is an interactive dashboard for *multi-modal, multi-model mental health assessment that integrates transcript text, audio, and facial behavior with a clinically grounded VLM prediction pipeline. |
P-QuASAR: A Unified Probabilistic Framework for Holistic Patent Quality Assessment and Refinement (2026.findings-acl)
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| Challenge: | Existing methods for assessing patent quality rely on modular pipelines or generic detectors, resulting in fragmented decisions and limited integration across quality dimensions. |
| Approach: | They propose a probabilistic framework that represents patent specifications as Quality Graphs. |
| Outcome: | The proposed framework outperforms existing methods on 500 patents against seven baselines. |
Learning Compressed Sentence Representations for On-Device Text Processing (P19-1)
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Dinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman, Xinyuan Zhang, Qian Yang, Meng Tang, Asli Celikyilmaz, Lawrence Carin
| Challenge: | Existing methods for learning sentence embeddings assume they are continuous and real-valued. |
| Approach: | They propose four different strategies to transform continuous and generic sentence embeddings into a binarized form while preserving their rich semantic information. |
| Outcome: | The proposed methods reduce storage requirements by over 98% and improve performance on downstream tasks. |
Improving Textual Network Embedding with Global Attention via Optimal Transport (P19-1)
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Liqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang, Lawrence Carin
| Challenge: | Existing methods for learning textual network embeddings are noisy and sparse. |
| Approach: | They propose to use text-based attention parsing to learn context-aware network embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art methods in a number of domains. |