Papers by Tiezheng Yu
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset (2022.lrec-1)
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Tiezheng Yu, Rita Frieske, Peng Xu, Samuel Cahyawijaya, Cheuk Tung Yiu, Holy Lovenia, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung
| Challenge: | In this paper, we address the problem of data scarcity for the Hong Kong Cantonese language . due to the popularization of deep learning, ASR technology has led to a significant improvement in recognizing many languages. |
| Approach: | They propose to use a dataset to analyze the data available for the Hong Kong Cantonese language . they use zh-HK as a source and a state-of-the-art ASR model to build a powerful model . |
| Outcome: | The proposed model improves on the biggest existing dataset, Common Voice zh-HK. |
Unified Thinker: A General Reasoning Core for Image Generation (2026.acl-long)
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Sashuai Zhou, Qiang Zhou, Jijin Hu, Hanqing Yang, Yue Cao, Junpeng Ma, Yinchao Ma, Jun Song, Tiezheng Ge, Cheng Yu, Bo Zheng, Zhou Zhao
| Challenge: | generative models struggle with logic-intensive instruction following, exposing a persistent reasoning–execution gap. |
| Approach: | They propose a task-agnostic reasoning architecture for general image generation . they propose pixel-level feedback to ground the Thinker's policy in pixel feedback . |
| Outcome: | The proposed system significantly improves image reasoning and generation quality. |
Subtle Errors in Reasoning: Preference Learning via Error-injected Self-editing (2025.acl-long)
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Kaishuai Xu, Tiezheng Yu, Wenjun Hou, Yi Cheng, Chak Tou Leong, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li
| Challenge: | Existing studies to improve mathematical ability typically involve applying preference learning to step-wise solution pairs, but they overlook critical subtle errors. |
| Approach: | They propose a preference learning framework that injects predefined subtle errors into pivotal tokens to construct hard pairs for error mitigation. |
| Outcome: | Extensive experiments show that the proposed framework improves on Qwen2-7B-Instruct and MATH with 4.5K training samples. |
AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization (2021.naacl-main)
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| Challenge: | State-of-the-art abstractive summarization models rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available. |
| Approach: | They propose to use domain adaptation methods to simulate the low-resource domain adaptation setting for abstractive summarization systems with existing datasets across six diverse target domains. |
| Outcome: | The proposed model can be used to adapt to a low-resource domain adaptation setting. |
Multi-hop Question Generation with Graph Convolutional Network (2020.findings-emnlp)
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| Challenge: | Existing studies on text-based QG focus on generating SQuAD-style questions. |
| Approach: | They propose a multi-hop question generation model that does context encoding in multiple hops with Graph Convolutional Network and encoder fusion via an Encoder Reasoning Gate. |
| Outcome: | Empirical results show that the proposed model generates fluent questions with high completeness and outperforms baselines on automatic evaluation metrics. |
Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization (2021.emnlp-main)
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| Challenge: | Existing MAS models cannot leverage GPLMs’ powerful generation ability. |
| Approach: | They propose a method to construct vision guided (VG) GPLMs that incorporate visual information while maintaining their original text generation ability. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model by 5.7 ROUGE-1, 5.3 ROUGe-2, and 5.1 ROUGEL-L scores on the How2 dataset and contributes 83.6% of the overall improvement. |
Towards Mitigating LLM Hallucination via Self Reflection (2023.findings-emnlp)
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| Challenge: | Large language models have shown promise for generative and knowledge-intensive tasks including question-answering (QA) but the practical deployment still faces challenges, notably the issue of “hallucination”, where models generate plausible-sounding but unfaithful or nonsensical information. |
| Approach: | They propose a self-reflection methodology that incorporates knowledge acquisition and answer generation to address the issue of "hallucination" they use a set of LLMs to generate a more accurate and factually accurate answer. |
| Outcome: | The proposed approach improves factuality, consistency, and entailment of the generated answers. |
Generating Hashtags for Short-form Videos with Guided Signals (2023.acl-long)
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Tiezheng Yu, Hanchao Yu, Davis Liang, Yuning Mao, Shaoliang Nie, Po-Yao Huang, Madian Khabsa, Pascale Fung, Yi-Chia Wang
| Challenge: | Short-form video hashtag recommendation (SVHR) is a classification or ranking problem that selects hashtags from a set of limited candidates. |
| Approach: | They propose a short-form video hashtag recommendation task that better represents how hashtags are created naturally by retrieving relevant hashtags from a large-scale hashtag pool as extra guidance signals. |
| Outcome: | The proposed model outperforms strong classification baselines on two short-form video datasets and the guidance signals boost the performance by 8.11 and 2.17 absolute ROUGE-1 scores on average. |
NusaCrowd: Open Source Initiative for Indonesian NLP Resources (2023.findings-acl)
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Samuel Cahyawijaya, Holy Lovenia, Alham Fikri Aji, Genta Winata, Bryan Wilie, Fajri Koto, Rahmad Mahendra, Christian Wibisono, Ade Romadhony, Karissa Vincentio, Jennifer Santoso, David Moeljadi, Cahya Wirawan, Frederikus Hudi, Muhammad Satrio Wicaksono, Ivan Parmonangan, Ika Alfina, Ilham Firdausi Putra, Samsul Rahmadani, Yulianti Oenang, Ali Septiandri, James Jaya, Kaustubh Dhole, Arie Suryani, Rifki Afina Putri, Dan Su, Keith Stevens, Made Nindyatama Nityasya, Muhammad Adilazuarda, Ryan Hadiwijaya, Ryandito Diandaru, Tiezheng Yu, Vito Ghifari, Wenliang Dai, Yan Xu, Dyah Damapuspita, Haryo Wibowo, Cuk Tho, Ichwanul Karo Karo, Tirana Fatyanosa, Ziwei Ji, Graham Neubig, Timothy Baldwin, Sebastian Ruder, Pascale Fung, Herry Sujaini, Sakriani Sakti, Ayu Purwarianti
| Challenge: | Existing NLP research in Indonesian languages has been held back by factors such as language diversity, orthographic variation, resource limitation and other societal challenges. |
| Approach: | They present a collaborative initiative to collect and unify existing resources for Indonesian languages and open access to previously non-public resources. |
| Outcome: | The results show that the datasets are highly reliable and can be used to generate the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. |
Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance (2021.findings-acl)
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| Challenge: | Query focused summarization models aim to generate summaries from source documents that can answer the given query. |
| Approach: | They propose a QFS-BART model that incorporates the explicit answer relevance of the source documents given the query via a question answering model. |
| Outcome: | Empirical results show that the proposed model achieves the new state-of-the-art performance. |
CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command Recognition (2022.lrec-1)
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Wenliang Dai, Samuel Cahyawijaya, Tiezheng Yu, Elham J. Barezi, Peng Xu, Cheuk Tung Yiu, Rita Frieske, Holy Lovenia, Genta Winata, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung
| Challenge: | In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety. |
| Approach: | They propose a dataset for in-car command recognition in the cantonese language with both video and audio data. |
| Outcome: | The proposed model can achieve a considerable quality on the clean test set, but the speech recognition quality on noisy data is still inferior. |
ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation (2022.lrec-1)
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Holy Lovenia, Samuel Cahyawijaya, Genta Winata, Peng Xu, Yan Xu, Zihan Liu, Rita Frieske, Tiezheng Yu, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung
| Challenge: | Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. |
| Approach: | They propose to collect Mandarin Chinese-English code-switching corpus from read speech rather than spontaneous speech to address this phenomenon. |
| Outcome: | ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English. |
NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias (2022.naacl-main)
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| Challenge: | a new task is proposed to reduce media news framing bias by generating a neutral summary from multiple news articles of the varying political leanings. |
| Approach: | They propose a task that generates a neutral summary from multiple news articles . they find title provides a good signal for framing bias and propose metric and model . |
| Outcome: | The proposed task can neutralize news content in hierarchical order from title to article . scalability remains a bottleneck due to the time-consuming human labor needed for composing the roundup . |
Learning to Align Multi-Faceted Evaluation: A Unified and Robust Framework (2025.findings-acl)
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Kaishuai Xu, Tiezheng Yu, Yi Cheng, Wenjun Hou, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li
| Challenge: | Existing methods for fine-tuning open-source LLMs are limited to text-based analysis under predefined general criteria. |
| Approach: | They propose a framework that fine-tunes LLMs to replicate the evaluation explanations and judgments of proprietary models. |
| Outcome: | The proposed evaluation framework outperforms existing fine-tuned evaluation methods in effectiveness and robustness. |
RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding (2023.findings-acl)
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| Challenge: | Existing knowledge-grounded dialogue systems generate accurate and informative responses, but they are prone to hallucination problems. |
| Approach: | They propose a method to generate hallucinated responses using knowledge graphs . they propose local knowledge grounding to combine textual embeddings with corresponding KG embeddments . a global knowledge ground technique is also proposed to equip RHO with multi-hop reasoning abilities . |
| Outcome: | The proposed approach outperforms state-of-the-art methods on automatic and human evaluation by a large margin. |
Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition (2020.aacl-main)
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| Challenge: | despite recent advances in multimodal emotion recognition, two problems still exist: sub-optimal performance and low-resource emotions. |
| Approach: | They propose a modality-transferable model with emotion embeddings to solve these problems . they use pre-trained word embedders to represent emotion categories for textual data . |
| Outcome: | The proposed model outperforms baselines in zero-shot and few-shot scenarios for unseen emotions. |