Papers by Chang Tian
Rethinking Data Selection at Scale: Random Selection is Almost All You Need (2025.findings-emnlp)
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| Challenge: | Existing data selection techniques are designed for small data pools, a study finds . filtering data by token length is an efficient method for improving results . |
| Approach: | They use self-scoring methods that do not rely on external help to perform fine-tuning . they also find that filtering data by token length offers a stable and efficient method . |
| Outcome: | The proposed methods outperform random selection on large datasets on large data pools. |
Detoxifying Large Language Models via the Diversity of Toxic Samples (2025.emnlp-main)
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| Challenge: | Existing methods for analyzing and utilizing toxic samples are limited . current methods fail to fully harness their potential . |
| Approach: | They propose a diverse detoxification framework that leverages toxic samples' diversity . they propose MPSG strategy and SC-DPO approach to elicit personalized toxic responses . |
| Outcome: | The proposed framework could be used to optimize large language models for user safety . it incorporates two components: MPSG strategy and SC-DPO approach . |
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies (2023.eacl-main)
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| Challenge: | Existing labeled datasets are heavily imbalanced, limiting the QA performance in this domain. |
| Approach: | They propose a question answering task that captures relevant text segments from unlabeled policy documents and expands the positive examples in the training set. |
| Outcome: | The proposed framework elevates the baseline by a large margin (10% F1) and achieves a new state-of-the-art F1 score of 50%. |
Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learners (2024.acl-long)
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Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang, Jinchuan Tian, Zhenhui Ye, Luping Liu, Zehan Wang, Ziyue Jiang, Xuankai Chang, Jiatong Shi, Chao Weng, Zhou Zhao, Dong Yu
| Challenge: | Large language models (LLMs) have been used for general-purpose interfaces across multiple tasks and languages. |
| Approach: | They propose to use large language models as a general-purpose interface across multiple tasks and languages. |
| Outcome: | The proposed model performs better on 200K hours of 6-language data for voice generation applications. |
Conditional Supervised Contrastive Learning for Fair Text Classification (2022.findings-emnlp)
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| Challenge: | Recent advances in natural language processing have demonstrated societal bias in existing NLP models. |
| Approach: | They propose to use contrastive learning to learn fair representations for text classification . they conduct experiments on two text datasets to demonstrate their methods are stable . |
| Outcome: | The proposed methods balancing task performance and bias mitigation are stable in different hyperparameter settings. |
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance (2026.acl-long)
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| Challenge: | Current approaches to writing effective rebuttals are limited by the direct-to-text generation problem . authors must accurately decipher reviewer intent while ensuring every response is firmly anchored in verifiable manuscript details. |
| Approach: | They propose a framework that reframes rebuttal generation as an evidence-centric planning task. |
| Outcome: | The proposed framework outperforms baselines in coverage, faithfulness, and strategic coherence. |
FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification (2022.emnlp-main)
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| Challenge: | Recent research shows keyword-driven methods can achieve state-of-the-art performance on various tasks. |
| Approach: | They propose an efficient weakly-supervised text classification approach using unlabeled data . they use dense text representation to retrieve class-relevant documents from unlabed corpus . |
| Outcome: | The proposed weakly-supervised classification method outperforms keyword-driven models on a wide range of classification tasks. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
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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 . |
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue (2022.emnlp-main)
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| Challenge: | Existing generative replay methods use only a single task-specific token to control their models. |
| Approach: | They propose a method to capture task-specific distributions with a conditional variational autoencoder, conditioned on natural language prompts to guide the pseudo-sample generation. |
| Outcome: | The proposed method outperforms baselines on natural language understanding tasks of advanced task-oriented dialogue (ToD) systems. |
McBE: A Multi-task Chinese Bias Evaluation Benchmark for Large Language Models (2025.findings-acl)
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| Challenge: | Existing datasets on bias evaluation for large language models focus on English and North American culture and are limited to one task. |
| Approach: | They propose to evaluate Chinese language models' biases from multiple perspectives using a multi-task Chinese Bias Evaluation Benchmark. |
| Outcome: | The proposed model covers 12, 82 subcategories and 5 evaluation tasks covering a wide range of categories and content diversity. |
PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English (2023.acl-short)
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| Challenge: | Existing efforts to understand privacy policies are limited by processing the language in a way exclusive to a single task focusing on certain privacy practices. |
| Approach: | They propose a privacy policy language understanding evaluation benchmark to evaluate the understanding of privacy policies across multiple tasks. |
| Outcome: | The proposed framework improves the understanding of privacy policies across multiple tasks. |
Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues (2022.acl-long)
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Qingxiu Dong, Ziwei Qin, Heming Xia, Tian Feng, Shoujie Tong, Haoran Meng, Lin Xu, Zhongyu Wei, Weidong Zhan, Baobao Chang, Sujian Li, Tianyu Liu, Zhifang Sui
| Challenge: | Existing work in vision language cross-modal reasoning uses binary or multi-choice classification based on source image and textual query. |
| Approach: | They propose a task where a textual premise is the background presumption on each source image. |
| Outcome: | The proposed task is based on a dataset of 15,360 movie screenshots and human-curated premise templates from 6 pre-defined categories. |
Intent Classification and Slot Filling for Privacy Policies (2021.acl-long)
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| Challenge: | Sentences written in privacy policies explain privacy practices and the constituent text spans convey further specific information. |
| Approach: | They propose an English corpus of 5,250 intent and 11,788 slot annotations . they propose two alternative neural approaches to model the corpus as a sequence-to-sequence learning task. |
| Outcome: | The proposed corpus predicts intent classification and slot filling, while the sequence tagging method outperforms slot filler by a large margin. |
PolicyQA: A Reading Comprehension Dataset for Privacy Policies (2020.findings-emnlp)
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| Challenge: | Privacy policy documents are long and verbose. Hence, a question answering system can help users find the information that is relevant and important to them. |
| Approach: | They propose to provide users with a short text span from policy documents to search for answers from a long text segment. |
| Outcome: | The proposed question answering system can help users find information relevant to them. |
Towards Robust Speech Representation Learning for Thousands of Languages (2024.emnlp-main)
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William Chen, Wangyou Zhang, Yifan Peng, Xinjian Li, Jinchuan Tian, Jiatong Shi, Xuankai Chang, Soumi Maiti, Karen Livescu, Shinji Watanabe
| Challenge: | XEUS is a cross-lingual encoder for universal speech that can be trained on 1 million hours of data across 4057 languages. |
| Approach: | They propose a Cross-lingual Encoder for Universal Speech that can be trained on 1 million hours of data across 4057 languages and a newly created corpus of 7400+ hours from 4057 . |
| Outcome: | The proposed model outperforms state-of-the-art models on several benchmarks and outperfies MMS 1B and w2v-BERT 2.0 v2 by 0.8% and 4.4% respectively. |
ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Existing methods to find relational facts from texts lack hierarchical information of relations. |
| Approach: | They propose a hierarchical classification framework which extracts relation in a top-down manner. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on NYT dataset . the proposed method generates large amounts of training data by aligning KBs with unlabeled corpora . |
Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction (2026.findings-acl)
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Shuoxin Wang, Chang Liu, Gowen Loo, Lifan Zheng, Kaiwen Wei, Huanqian Yan, Xinyi Zeng, Jingyuan Zhang, Yu Tian
| Challenge: | Existing Large Language Model (LLM)-based mobile agents follow explicit user instructions without personalized needs. |
| Approach: | They propose a user preference learning strategy enhanced with a Personal Reward Model to improve personalization performance. |
| Outcome: | The proposed agent achieves state-of-the-art performance while maintaining competitive instruction execution performance. |
Aspect-based Sentiment Analysis with Context Denoising (2024.findings-naacl)
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| Challenge: | Existing approaches to ABSA use text encoders to locate important context features or remove them from input. |
| Approach: | They propose to improve ABSA with context denoising to remove noise from text . they use diffusion networks to perform denoizing process to gradually eliminate noise . paper shows that aspect-based sentiment analysis is effective for fine-grained analysis . |
| Outcome: | The proposed approach improves ABSA on five widely used ABSA datasets. |
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction (2020.acl-main)
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| Challenge: | Existing approaches to extract relational triples from unstructured text are inadequate to solve the overlapping triple problem. |
| Approach: | They propose a cascade binary tagging framework that models relations as functions that map subjects to objects in a sentence. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two datasets . it outperformed baseline methods by 17.5 and 30.2 absolute gains . |
Anti-Overestimation Dialogue Policy Learning for Task-Completion Dialogue System (2022.findings-naacl)
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| Challenge: | Recent research has focused on reinforcement learning (RL)-based dialogue policy. |
| Approach: | They propose a dynamic partial average estimator (DPAV) of the ground truth maximum action value to solve the overestimation problem. |
| Outcome: | The proposed method achieves better results on three dialogue datasets with a lower computational load compared to baselines on three different domains with lower bias. |
A Generic Method for Fine-grained Category Discovery in Natural Language Texts (2024.emnlp-main)
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| Challenge: | Existing methods for fine-grained category discovery neglect semantic similarities of fine-grain categories. |
| Approach: | They propose a method that detects fine-grained clusters of semantically similar texts guided by a novel objective function. |
| Outcome: | The proposed method surpasses state-of-the-art methods on three benchmark tasks. |
Towards Unified Representations of Knowledge Graph and Expert Rules for Machine Learning and Reasoning (2022.aacl-main)
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| Challenge: | Empirical study shows superiority of proposed method over time-tested knowledge-driven and data-driven methods. |
| Approach: | They propose a cognitive knowledge graph that unifies expert rules and relational facts as the substrate of machine learning and reasoning models. |
| Outcome: | Empirical results show the proposed method superior to time-tested methods . the proposed model can perform both learning and reasoning with labeled data . |