Papers by Ting Zhou
A Self-verified Method for Exploring Simile Knowledge from Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) have succeeded in natural language processing because they learn generic knowledge from a large corpus. |
| Approach: | They propose a method that allows pre-trained language models to explore simile knowledge from PLMs . they enhance PLM models with a multi-level simile recognition task that evaluates similes aplenty . |
| Outcome: | The proposed method can explore more accurate simile knowledge for PLMs. |
GCNet: Global-and-Context Collaborative Learning for Aspect-Based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence. |
| Approach: | They propose a GCNet that explicitly leverages global semantic information to guide context encoding. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets. |
Interpreting Twitter User Geolocation (2020.acl-main)
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| Challenge: | Existing methods for identifying user geolocation suffer from a lack of interpretability on the corresponding results. |
| Approach: | They adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting locations. |
| Outcome: | The proposed method provides meaningful explanations on prediction results and also uncovers the so-called "black-box" GNN-based models by investigating the effect of individual nodes. |
LEAF: Towards Lightweight Explainable Hateful Video Detection via Self-Grounding CoT Guided Stage-Wise Distillation (2026.findings-acl)
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| Challenge: | Existing methods for detecting hateful videos rely on opaque models with no insight into their decisions. |
| Approach: | They propose a lightweight, explainable video detection framework that distills "explainability" from LMMs into efficient Smaller Multimodal Models (SMMs) they use a self-grounded chain-of-thought mechanism to generate unbiased supervision signals for videos . |
| Outcome: | The proposed framework outperforms existing methods in detection accuracy and explainability on three video benchmarks. |
A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction (2020.acl-main)
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| Challenge: | Existing models for machine reading comprehension lack evidence labels for training models. |
| Approach: | They propose a method which supervises the evidence extractor with auto-generated evidence labels in an iterative process. |
| Outcome: | The proposed method improves on three MRC tasks on seven datasets. |
CXR Data Annotation and Classification with Pre-trained Language Models (2022.coling-1)
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Nina Zhou, Ai Ti Aw, Zhuo Han Liu, Cher heng Tan, Yonghan Ting, Wen Xiang Chen, Jordan sim zheng Ting
| Challenge: | Existing tools for clinical data annotation are limited to specific institutions due to differences in writing style, structure, language use and label definition. |
| Approach: | They propose a weak supervision annotation framework with two improvements over existing ones . the framework provides an efficient form of sample selection and data auto-annotation . |
| Outcome: | The proposed framework provides better results for clinical data annotation tasks compared to existing frameworks. |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. |
| Approach: | They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. |
| Outcome: | The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication. |
Verb Metaphor Detection via Contextual Relation Learning (2021.acl-long)
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| Challenge: | Recent work on verb metaphor detection focuses on analyzing restricted forms of linguistic context. |
| Approach: | They propose a model which explicitly models the relation between a verb and its various contexts. |
| Outcome: | The proposed model gets competitive results compared with state-of-the-art approaches on the VUA, MOH-X and TroFi datasets. |
ORANGE: Text-video Retrieval via Watch-time-aware Heterogeneous Graph Contrastive Learning (2023.emnlp-industry)
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Yucheng Lin, Tim Chang, Yaning Chang, Jianqiang Ma, Donghui Li, Ting Peng, Zang Li, Zhiyi Zhou, Feng Wang
| Challenge: | Existing methods for text-video retrieval focus on informative representations and delicate matching mechanisms, but real-world scenarios often involve brief, ambiguous queries and low-quality videos. |
| Approach: | They propose a novel method to learn informative embeddings for queries and videos . they use a watch-time-aware contrastive learning paradigm to capture dependencies . |
| Outcome: | The proposed method is effective in a real-world video-search service. |
SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework (2026.acl-long)
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Chengran Yang, Ting Zhang, Jinfeng Jiang, Xin Zhou, Haoye Tian, Mingzhe Du, Jieke Shi, Junkai Chen, Yikun Li, Eng Lieh Ouh, Lwin Khin Shar, David Lo
| Challenge: | Existing methods for automating vulnerability repair suffer from syntactic overfitting . nvd published 49,230 Common Vulnerabilities and Exposures (CVE) records in 2025 alone . |
| Approach: | They propose a semantic-aware reward framework that optimizes for code semantic equivalence rather than lexical mimicry. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on repository-level splits . it incorporates expert-aligned reasoning mechanism that grounds patch generation in structured diagnosis. |
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction (2022.acl-long)
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| Challenge: | Existing FET noise learning methods rely on prediction distributions in instance-independent manner, which causes confirmation bias. |
| Approach: | They propose a clustering-based loss correction framework to address confirmation bias in FET . they first train a coarse backbone model as a feature extractor and noise estimator . |
| Outcome: | The proposed framework achieves the best performance over existing systems on three public datasets and is stable to hyperparameters. |
Context-Sensitive Generation of Open-Domain Conversational Responses (C18-1)
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| Challenge: | Existing studies on single-turn conversation generation focus on coherence and context-sensitive generation of open-domain conversational responses. |
| Approach: | They propose static and dynamic attention based approaches for context-sensitive generation of open-domain conversational responses. |
| Outcome: | The proposed model outperforms all baselines on automatic and human evaluation on two public datasets. |
Somali Information Retrieval Corpus: Bridging the Gap between Query Translation and Dedicated Language Resources (2023.emnlp-main)
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| Challenge: | Existing research on the Somali language information retrieval relies on query translation . lack of digital resources is key obstacle to advancing language technologies . |
| Approach: | They develop an annotated corpus for Somali information retrieval using query expansion technique. |
| Outcome: | The proposed corpus comprises 2335 documents collected from well-known online sites . it can be used for text classification-related tasks and question-answering research purposes. |
Causal-Debias: Unifying Debiasing in Pretrained Language Models and Fine-tuning via Causal Invariant Learning (2023.acl-long)
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| Challenge: | Existing methods to remove unwanted stereotypical associations from pretrained language models (PLMs) are often focused on removing unwanted stereotypes from PLMs. |
| Approach: | They propose a framework to remove unwanted stereotypical associations in pretrained language models . they propose bias-relevant factors are causal, while labelrelevant factors causal . |
| Outcome: | The proposed framework reduces stereotypical associations after PLMs are fine-tuned . the proposed framework mitigates bias from a causal invariant perspective . |
CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)
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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou
| Challenge: | Large pre-trained models have improved performance on a variety of natural language processing tasks. |
| Approach: | They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators. |
| Outcome: | The proposed model performs better on two NL-PL applications by fine-tuning model parameters. |
Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)
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Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou
| Challenge: | Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents. |
| Approach: | They propose a generative model to map natural language questions into SQL queries. |
| Outcome: | The proposed model significantly improves state-of-the-art execution accuracy from 69.0% to 74.4% on a large question- SQL dataset. |
Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning (2023.eacl-main)
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| Challenge: | Multi-hop reasoning is a common approach for query answering, but can be biased to spurious paths which coincidentally lead to the correct answer with poor explanation. |
| Approach: | They propose a method that quantitatively estimates to what extent a path is spurious by a metric called Path Spuriousness (PS) they propose KG reasoning, which infers new facts along existing paths in KGs. |
| Outcome: | The proposed model significantly improves the agent’s ability to prevent spurious paths while keeping comparable to state-of-the-art performance. |
AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code (2026.findings-acl)
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ShangZhan Li, Xinyu Yin, Xuanyu Jin, Ye He, Yuxin Zhou, Yuxuan Li, Xu Han, Wanxiang Che, Qi Shi, Ting Liu, Maosong Sun
| Challenge: | Current development practices face a dichotomy between automation and performance. |
| Approach: | They propose a framework to empower LLMs with the capability of automated explicit vectorization. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the SSE and AVX subsets of SimdBench. |
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network (2020.acl-main)
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| Challenge: | Existing few-shot learning methods for slot tagging are based on similarity-based methods, but they are difficult to apply to an unseen domain due to the discrepancy of label sets. |
| Approach: | They propose a label-enhanced task-adaptive projection network to transfer abstract label dependency patterns as transition scores into the conditional random field (CRF) Experimental results show that their model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting. |
| Outcome: | The proposed model outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting. |
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing approaches to machine reading comprehension treat documents at their hierarchical nature, ignoring their dependencies. |
| Approach: | They propose a machine reading comprehension benchmark with two-grained answers . they use graph attention networks to model documents at their hierarchical nature . |
| Outcome: | The proposed framework outperforms existing systems at long and short answer criteria. |
Bitnet.cpp: Efficient Edge Inference for Ternary LLMs (2025.acl-long)
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Jinheng Wang, Hansong Zhou, Ting Song, Shijie Cao, Yan Xia, Ting Cao, Jianyu Wei, Shuming Ma, Hongyu Wang, Furu Wei
| Challenge: | 1-bit large language models have spurred interest in ternary LLMs, but efficient edge inference is still scarce. |
| Approach: | They propose an inference system optimized for 1-bit large language models . they propose a new library that facilitates sub-2-bits-per-weight inference . |
| Outcome: | The proposed inference system achieves 6.25x speed increase over full-precision baselines and 2.32x over low-bit baselines. |
Exploring and Distilling Multi-Dimensional Clues for Interpretable Social Bot Detection (2026.acl-long)
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| Challenge: | Existing research on social bot detection results directly without corresponding supportive explanations, making it difficult to assess the extent to which such predictions are trustworthy. |
| Approach: | They propose a four-dimensional clue framework that uses outcome-reward reinforcement learning to train inspectors to generate faithful, grounded clues from user information, semantic features, interactive situation, and behavioral pattern. |
| Outcome: | The proposed framework outperforms baselines in detection performance and significantly improves the performance of large language models. |
I run as fast as a rabbit, can you? A Multilingual Simile Dialogues Datasets (2023.findings-acl)
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| Challenge: | A simile is a figure of speech that compares two different things via shared properties. |
| Approach: | They propose a multilingual simile dialogue dataset that can be used to study similes in real-life scenarios. |
| Outcome: | The proposed dataset is the largest manually annotated simile dataset and contains both English and Chinese data. |
SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios (2026.acl-long)
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Junkai Chen, Huihui Huang, Yunbo Lyu, Junwen An, Jieke Shi, Chengran Yang, Ting Zhang, Haoye Tian, Yikun Li, Zhenhao Li, Xin Zhou, Xing Hu, David Lo
| Challenge: | Existing benchmarks fail to capture scenarios in which vulnerabilities are introduced by humans . we evaluate 5 popular code agents supported by 5 LLMs on SecureVibeBench . |
| Approach: | They propose a benchmarking tool that compares 105 C/C++ secure coding tasks . they use real-world open-source vulnerabilities and a comprehensive evaluation tool . |
| Outcome: | The proposed benchmarks show that code agents struggle to produce correct and secure code . the best performing agent produces merely 23.8% correct and secured solutions . |
Query-aware Multi-modal based Ranking Relevance in Video Search (2023.emnlp-industry)
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| Challenge: | Existing relevance ranking methods focus on text modality, incapable of fully exploiting cross-modal cues present in video. |
| Approach: | They propose a QUery-Aware pre-training model with multi-modality that integrates video tag information as alignment targets and enhances ranking optimization method based on ordinal regression. |
| Outcome: | The proposed model significantly improves video search performance. |