Papers by Bowei Zou
GLGR: Question-aware Global-to-Local Graph Reasoning for Multi-party Dialogue Reading Comprehension (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches for multi-hop reasoning are lacking for local graph reasoning . existing approaches neglect local semantic structures in utterances . |
| Approach: | They propose a question-aware global-to-local graph reasoning approach that expands the canonical Interlocutor-Utterance graph by introducing a query node. |
| Outcome: | The proposed approach outperforms existing methods on Molweni and FriendsQA. |
Negative Focus Detection via Contextual Attention Mechanism (D19-1)
Copied to clipboard
| Challenge: | Negation is a universal but complicated linguistic phenomenon that reverses the polarity of a statement or its property into opposite. |
| Approach: | They propose a framework which consists of a Bidirectional Long Short-Term Memory neural network and a Conditional Random Fields layer to capture contextual information. |
| Outcome: | The proposed framework improves on the SEM’12 shared task corpus, yielding an absolute improvement of 2.11% over the state-of-the-art. |
Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Attributed Question Answering models are not yet leveraged to enhance their essential capabilities, including evidence identification, cross-source relation recognition and anti-distraction reasoning. |
| Approach: | They propose a progressive progressive curriculum learning approach that optimizes both encoder-decoder and decoder-only AQA models. |
| Outcome: | The proposed approach improves both encoder-decoder and decoder-only AQA models on the quotesum benchmark. |
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension (2020.coling-main)
Copied to clipboard
| Challenge: | Existing RC models focus on extractive or generative, but ignore integration of them. |
| Approach: | They propose a noisy user-generated text-oriented RC model that integrates extractive and generative RC models by a multi-task learning mechanism and an answer selection module. |
| Outcome: | The proposed model outperforms state-of-the-art models on Twitter. |
A Benchmark for Translations Across Styles and Language Variants (2025.findings-emnlp)
Copied to clipboard
| Challenge: | lack of comprehensive evaluation benchmarks has hindered progress in this field . lack of evaluation benchmarking has hinder MT's ability to generate accurate outputs . |
| Approach: | They evaluate translations across semantic preservation, cultural and regional specificity, expression style, and fluency at both the word and sentence levels. |
| Outcome: | The proposed evaluation framework is validated on translations of state-of-the-art large language models . |
GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to training DST on a single domain ignore information across domains. |
| Approach: | They construct a dialogue state graph to transfer structured features among related domain-slot pairs across domains and encode the graph information of dialogue states by graph convolutional networks. |
| Outcome: | The proposed model improves the performance of the multi-domain DST baseline with the absolute joint accuracy of 2.0% and 1.0% on the MultiWOZ 2.0 and 2.1 dialogue datasets. |
Leveraging Contrastive Learning and Knowledge Distillation for Incomplete Modality Rumor Detection (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing rumor detection models neglect the semantic coherence between text and image components in multimodal posts . Existing models neglect incomplete modalities in single modal posts, such as missing text or images . |
| Approach: | They propose a framework for incomplete modality rumor detection that captures semantic consistency between text and image pairs while enhancing model generalization to incomplete modalities within individual posts. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two English and two Chinese benchmark datasets for rumor detection in social media. |
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 . |
CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Multilingual Large Language Models (MLLMs) exhibit strong generalization across languages, yet they remain prone to hallucinations due to training data imbalances. |
| Approach: | They propose a cross-lingual Chain-of-Thought framework that enhances cross-linguistic alignment . the framework guides the model to reason in a high-resource language before generating answers in low-resourced language. |
| Outcome: | The proposed framework reduces hallucination rates by up to 62% and significantly improves factual knowledge transfer across language pairs. |
Winnowing Knowledge for Multi-choice Question Answering (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing reasoning models suffer from noises in retrieved knowledge . encoding methods that use commonsense knowledge are less effective . |
| Approach: | They propose a method which conducts interception and soft filtering to reduce noise . they use commonsense knowledge from Wikipedia and ConceptNet to encode questions and options . |
| Outcome: | The proposed method improves on commonsense question answering tasks compared to baselines . it is able to conduct interception and soft filtering to shield the encoder from noise . |
Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition (C18-1)
Copied to clipboard
| Challenge: | Event relation recognition is a challenging language processing task because the query events are selected from different paragraphs in a document or even different documents, so there is lack of explicit clue. |
| Approach: | They propose to use image processing to acquire similar event instances and use image matching to approximate calculation between events. |
| Outcome: | The proposed model performs comparable to CNN while slightly better than LSTM on the ACE-R2 corpus. |
Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for complex question answering are limited in the search space of all possible relation paths. |
| Approach: | They propose a method that directly generates an executable SPARQL query without simplification. |
| Outcome: | The proposed method significantly outperforms the previous methods and has higher interpretability and computational efficiency than the previous ones. |
Empowering Tree-structured Entailment Reasoning: Rhetorical Perception and LLM-driven Interpretability (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing models for science question answering lack a framework for entailment trees . ambiguities and similarities between science facts complicate the fact retrieval process . |
| Approach: | They propose a framework for building entailment trees for science question answering . they propose to infuse knowledge that bridges the gap between reasoning types and rhetorical relations . |
| Outcome: | The proposed framework improves retrieval capabilities, understanding relationships and generating intermediate conclusions. |
Interview Evaluation: A Novel Approach for Automatic Evaluation of Conversational Question Answering Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing evaluation methods for CQA use pre-collected human-human conversations . previous methods use model-predicted dialogue history instead of ground truth . |
| Approach: | They propose an automatic evaluation approach that uses the model's dialogue history to evaluate models. |
| Outcome: | The proposed method improves on existing models and their evaluations on QuAC and CoQA. |
Adversarial Feature Adaptation for Cross-lingual Relation Classification (C18-1)
Copied to clipboard
| Challenge: | Existing methods for relation classification exploit monolingual data due to lack of annotated data in other languages. |
| Approach: | They propose an adversarial feature adaptation approach for cross-lingual relation classification using a generative adversarial network. |
| Outcome: | The proposed approach yields an improvement of 5.7% over the state-of-the-art. |
Making Pre-trained Language Models Better Learn Few-Shot Spoken Language Understanding in More Practical Scenarios (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing few-shot Spoken Language Understanding models need to be trained on a set of data-rich source domains and adapt to the target domain with a few examples. |
| Approach: | They propose a scenario where only a pre-trained language model and a few labeled examples are used to train few-shot SLU models. |
| Outcome: | The proposed model outperforms existing models on few-shot settings by reducing the number of slot labels and reducing training complexity. |
Multi-grained Chinese Word Segmentation with Weakly Labeled Data (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on single-grained word segmentation (SWS) focuses on segmenting a sentence into multiple word sequences to preserve all words of different granularities. |
| Approach: | They propose to use a graph-based parser to accommodate weakly labeled data for MWS by employing a simple yet competitive graph-basic parsers with local loss. |
| Outcome: | The proposed model outperforms the state-of-the-art model on weakly labeled data on a high-quality dataset from canonical newswire (NEWS) and non-canonical web (BAIKE) data. |
DSPM-NLG: A Dual Supervised Pre-trained Model for Few-shot Natural Language Generation in Task-oriented Dialogue System (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing models for few-shot natural language generation are based on a dual correlation between NLG and SLU from the perspective of probability. |
| Approach: | They propose a dual supervised pre-trained model to regularize the pre-training process . they use a probabilistic approach to learn the dual correlation between NLG and SLU . |
| Outcome: | The proposed model outperforms the previous state-of-the-art models on a few-shot dataset. |
Improving Explainable Fact-Checking with Claim-Evidence Correlations (2025.coling-main)
Copied to clipboard
| Challenge: | Existing fact-checking systems that employ large language models fail to reveal reasoning principles behind their decision-making for the claim verdict. |
| Approach: | They propose an LLM-based fact-checking system that simulates human reasoning principles . they propose a test set to evaluate the CorXFact system in real-world and closed-domain scenarios . |
| Outcome: | The proposed system outperforms four strong fact-checking baselines in claim authenticity prediction and verdict explanation. |
Enhancing Event-centric News Cluster Summarization via Data Sharpening and Localization Insights (2025.acl-long)
Copied to clipboard
| Challenge: | Existing work on text summarization approaches are approaching or exceeding human excellence . |
| Approach: | They propose a framework that optimizes the balance between information volume and entropy in input texts. |
| Outcome: | The proposed framework optimizes information volume and entropy in input texts, achieving notable improvements in localized contexts. |
Don’t Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Existing aspect extraction methods suffer from boundary errors, but they hurt performance severely. |
| Approach: | They propose to use a pointer network to reposition the boundaries of extracted aspects . they conduct experiments on laptop and restaurant benchmark datasets . |
| Outcome: | The proposed method outperforms state-of-the-art methods on benchmark datasets . it achieves substantial improvements over baseline and outperformed existing methods . |
Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to generate conversational question are naive and do not account for the answer span. |
| Approach: | They propose a framework for generating a conversational question from a context. |
| Outcome: | The proposed framework achieves state-of-the-art in two different settings compared to existing models . it uses a sentence as the rationale and extracts the answer span from it . |
CoHS-CQG: Context and History Selection for Conversational Question Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing studies focus on single-turn question generation, but few studies have studied the challenges of multiturn QG. |
| Approach: | They propose a two-stage conversational question generation framework that shortens the context and history of the input and calculates relevance scores. |
| Outcome: | The proposed framework achieves state-of-the-art on CoQA in answer-aware and answer-unaware settings. |
Capturing Conversational Interaction for Question Answering via Global History Reasoning (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing studies have studied history-dependent reasoning for question answering . utilizing global conversation history for enhancement is gaining interest . |
| Approach: | They propose to establish long-distance dependency among global utterances in multi-turn conversation. |
| Outcome: | The proposed method improves on QuAC by 1%, yielding the F1 score of 73.7%. |
CLFFRD: Curriculum Learning and Fine-grained Fusion for Multimodal Rumor Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing multimodal rumor detection models overlook sample difficulty and order when training . Existing models overlook text-level difficulty, image-level and multimodal difficulty when training samples . |
| Approach: | They propose a curriculum learning framework that uses fine-grained fusion to detect rumors . they propose fusion-based methods that combine text and images to enhance semantic cohesion . |
| Outcome: | The proposed framework outperforms state-of-the-art models on English and Chinese benchmark datasets. |
Looking Beyond the One: Operationalizing and Eliciting Visual Ambiguity in VLLMs (2026.acl-long)
Copied to clipboard
| Challenge: | Visual question answering systems typically collapse ambiguity, committing to a single interpretation during decoding and evaluation. |
| Approach: | They operationalize ambiguity as the existence of multiple answer-supporting regions in an image . they show that ambiguities are already encoded in their internal representations . |
| Outcome: | The proposed approach makes ambiguity observable without exhaustive annotations . ambiguities are already encoded in models, but not reliably expressed in outputs despite hidden states . |
Comprehensive Abstractive Comment Summarization with Dynamic Clustering and Chain of Thought (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent work on news comment summarization has focused on extractive methods within constraints. |
| Approach: | They propose an enhanced fast clustering algorithm that maintains a dynamic similarity threshold to ensure high density of each comment cluster being built. |
| Outcome: | The proposed method improves the baseline methods and the test suite on real-world news comments. |