Papers by Philip Yu

HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction (2022.naacl-main)

Challenge:	Recent studies show that learning domain-specific language models are equally important for general-purpose and domain-based learning.
Approach:	They propose a domain-oriented learning task that combine the benefits of both general and domain-specific worlds.
Outcome:	The proposed task solves the problems in an aspect-based sentiment analysis task.

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Shuliang Liu, Xuming Hu, Chenwei Zhang, Shu’ang Li, Lijie Wen, Philip Yu

Enriching Non-Autoregressive Transformer with Syntactic and Semantic Structures for Neural Machine Translation (2021.eacl-main)

Challenge:	Existing methods to extract relational feature signals from natural language sentences use self-supervised clustering and classification that cause gradual drift problems.
Approach:	They propose a framework that derives hierarchical signals from relational feature space using cross hierarchy attention and effectively optimizes relation representation of sentences under exemplar-wise contrastive learning.
Outcome:	The proposed framework can extract the relationship between entities from natural language sentences without prior knowledge on relation scope or distribution.

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Ye Liu, Yao Wan, Jianguo Zhang, Wenting Zhao, Philip Yu

Machine Translation

Challenge:	Existing non-autoregressive models have boosted the efficiency of neural machine translation, but their performance is significantly worse than that of autoregressive counterparts.
Approach:	They propose to incorporate syntactic and semantic structures among natural languages into a non-autoregressive Transformer for the task of neural machine translation.
Outcome:	The proposed model achieves faster speed and keeps translation quality compared with other models.

Stronger, Lighter, Better: Towards Life-Long Attribute Value Extraction for E-Commerce Products (2024.findings-acl)

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Tao Zhang, Chenwei Zhang, Xian Li, Jingbo Shang, Hoang Nguyen, Philip Yu

Green & Sustainable NLP

Challenge:	Existing models for attribute value extraction struggle for parameter efficiency and reliability due to data contamination and catastrophic forgetting.
Approach:	They propose to decouple product type and attribute to promote de-contamination and parameter efficiency while scaling up.
Outcome:	The proposed model achieves state-of-the-art performance with affordable parameter size, least historical knowledge forgetting, and greatest robustness against noises.

Composed Variational Natural Language Generation for Few-shot Intents (2020.findings-emnlp)

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Congying Xia, Caiming Xiong, Philip Yu, Richard Socher

DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)

Challenge:	Existing methods to detect spoken language deteriorate drastically in discriminating the few-shot intents.
Approach:	They propose a composed variational natural language generator (CLANG) that generates natural examples for few-shot intents in imbalanced scenarios.
Outcome:	The proposed model achieves state-of-the-art on two real-world intent detection datasets.

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Wenting Zhao, Ye Liu, Tong Niu, Yao Wan, Philip Yu, Shafiq Joty, Yingbo Zhou, Semih Yavuz

Information Retrieval

Challenge:	Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources.
Approach:	They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation .
Outcome:	The proposed model outperforms previous approaches by a significant margin in QA tasks over text.

kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning (2024.naacl-long)

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Wenting Zhao, Ye Liu, Yao Wan, Yibo Wang, Qingyang Wu, Zhongfen Deng, Jiangshu Du, Shuaiqi Liu, Yunlong Xu, Philip Yu

Enhancing Cross-lingual Transfer via Phonemic Transcription Integration (2023.findings-acl)

Challenge:	Recent advances in task-oriented parsing involve formulating the task as a sequence-to-sequence problem, relying on a wealth of labeled data.
Approach:	They propose a task-oriented parsing framework that integrates nearest-neighbor learning with a nearest-nearest approach.
Outcome:	The proposed model can be used to synthesize computer programs based on a natural-language prompt without additional data or specialized prompts.

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Hoang Nguyen, Chenwei Zhang, Tao Zhang, Eugene Rohrbaugh, Philip Yu

Multilinguality

Challenge:	Previous cross-lingual transfer methods are limited to orthographic representation learning via textual scripts.
Approach:	They propose a phonemic transcription framework that incorporates phonemic translations as an additional linguistic modality beyond the orthographic transcriptions for cross-lingual transfer.
Outcome:	The proposed framework captures local one-to-one alignment between two different modalities and integrates bilingual dictionaries into multilingual contexts.

BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (N19-1)

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Hu Xu, Bing Liu, Lei Shu, Philip Yu

Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks (2020.coling-main)

Challenge:	Existing work on question-answering has limited training examples for RRC . question-announced questions are a key component of online commerce .
Approach:	They propose to turn customer reviews into a large source of knowledge that can be exploited to answer user questions.
Outcome:	The proposed approach improves review reading comprehension on popular language model BERT . it also improves aspect extraction and aspect sentiment classification tasks .

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Lichao Sun, Congying Xia, Wenpeng Yin, Tingting Liang, Philip Yu, Lifang He

Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning (2021.emnlp-main)

Challenge:	Recent work on data augmentation techniques that interpolate inputs and labels shows strong effectiveness in image classification.
Approach:	They propose to integrate mixup to transformer-based pre-trained architecture for NLP tasks while keeping the whole end-to-end training system.
Outcome:	The proposed framework improves on GLUEbenchmark and transformer-based learning models while keeping the whole end-to-end training system.

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Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu

Dense Hierarchical Retrieval for Open-domain Question Answering (2021.findings-emnlp)

Challenge:	Existing methods address few-shot intent detection tasks from two perspectives: data augmentation and task-adaptive training with pre-trained models.
Approach:	They propose a few-shot intent detection schema using contrastive pre-training and fine-tuning.
Outcome:	The proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings.

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Ye Liu, Kazuma Hashimoto, Yingbo Zhou, Semih Yavuz, Caiming Xiong, Philip Yu

Natural Language Interfaces

Challenge:	Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA) current dense retrievers require splitting documents into short passages that usually contain local, partial and sometimes biased context, and may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result.
Approach:	They propose a hierarchical framework which can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic specific to each passage.
Outcome:	The proposed framework significantly outperforms the original dense passage retriever and helps an end-to-end QA system outperfect the strong baselines on multiple open-domain QA benchmarks.

Reimagining Safety Alignment with An Image (2025.emnlp-main)

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Yifan Xia, Guorui Chen, Wenqian Yu, Zhijiang Li, Philip Torr, Jindong Gu

Responsible & Trustworthy NLP

Challenge:	Existing approaches to large language models face inefficiency, fragility, or architectural constraints, resulting in inefficient performance and heightened over-refusal in cross-modal tasks.
Approach:	They propose an optimization-driven visual prompt framework that enhances security and reduces over-refusal at the same time.
Outcome:	The proposed framework enhances security and reduces over-refusal while maintaining robust safety while reducing unnecessary denials.

Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots (2021.findings-emnlp)

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Wenting Zhao, Ye Liu, Yao Wan, Philip Yu

Text Generation

Challenge:	Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data.
Approach:	They propose a novel approach to generate faithful table-to-text sentences using limited data . they aim to exploit table structure and natural linguistic information to generate accurate sentences .
Outcome:	The proposed approach generates higher qualified sentences when compared with state-of-the-art models on humans, songs, and books.

Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking (2020.starsem-1)

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Jianguo Zhang, Kazuma Hashimoto, Chien-Sheng Wu, Yao Wang, Philip Yu, Richard Socher, Caiming Xiong

HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization (2021.naacl-main)

Challenge:	Existing methods for dialog state tracking are ontology-based and ontologie-free . however, it is not clear enough which slots are better handled by either of the two methods .
Approach:	They propose a dual-strategy model that integrates both ontology-based and ontological-free methods.
Outcome:	The proposed model outperforms the existing model on noisy and cleaner datasets.

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Zhongfen Deng, Hao Peng, Dongxiao He, Jianxin Li, Philip Yu

MarkLLM: An Open-Source Toolkit for LLM Watermarking (2024.emnlp-demo)

Challenge:	Existing models for hierarchical text classification do not consider statistical constraint on label representations learned by structure encoder.
Approach:	They propose a new hierarchical text classification model called HTCInfoMax which incorporates two modules to improve the model's representations.
Outcome:	The proposed model can model the interaction between each text sample and its ground truth labels explicitly which filters out irrelevant information.

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Leyi Pan, Aiwei Liu, Zhiwei He, Zitian Gao, Xuandong Zhao, Yijian Lu, Binglin Zhou, Shuliang Liu, Xuming Hu, Lijie Wen, Irwin King, Philip Yu

Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference (2020.emnlp-main)

Challenge:	Large Language Models (LLMs) embed imperceptible yet algorithmically detectable signals in outputs to identify LLM-generated text.
Approach:	They propose to develop an open-source toolkit for LLM watermarking that embeds imperceptible yet algorithmically detectable signals in model outputs to identify LLM-generated text.
Outcome:	MarkLLM provides a unified framework for implementing LLM watermarking algorithms, while providing user-friendly interfaces to ensure ease of access.

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Jianguo Zhang, Kazuma Hashimoto, Wenhao Liu, Chien-Sheng Wu, Yao Wan, Philip Yu, Richard Socher, Caiming Xiong

User Memory Reasoning for Conversational Recommendation (2020.coling-main)

Challenge:	Existing work on few-shot intent classification without OOS has focused on the few-shot intent classification with out-of-scope intents.
Approach:	They propose to use BERT-style pairwise encoding to train a binary classifier that estimates the best matched training example for a user input.
Outcome:	The proposed approach achieves more stable and accurate in-domain and OOS detection accuracy than RoBERTa-based classifiers and embedding-based nearest neighbor approaches.

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Hu Xu, Seungwhan Moon, Honglei Liu, Bing Liu, Pararth Shah, Bing Liu, Philip Yu

Multimodality

Challenge:	Existing systems that update user preferences via asking relevant questions are unable to dynamically maintain and reason over their knowledge for current (and possibly future) recommendations.
Approach:	They propose a new memory graph (MG) -> Conversational Recommendation parallel corpus with 7K+ human-to-human role-playing dialogs and a graph-based reasoning model that updates MG from unstructured utterances and predicts optimal dialog policies based on updated MG.
Outcome:	The proposed model is based on a large-scale user memory bootstrapped from real-world user scenarios and can be easily updated from unstructured utterances.

ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction (2024.findings-acl)

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Henry Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip Yu, Cornelia Caragea

HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization (2021.emnlp-main)

Challenge:	Existing datasets for attribute value extraction focus on explicit attribute values while neglecting the implicit ones.
Approach:	They present a multimodal dataset for implicit attribute value extraction that includes AVE and multimodality.
Outcome:	The proposed dataset includes 68k training and 1.6k testing data across five domains.

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Ye Liu, Jianguo Zhang, Yao Wan, Congying Xia, Lifang He, Philip Yu

DA3: A Distribution-Aware Adversarial Attack against Language Models (2024.emnlp-main)

Challenge:	Existing methods for summarizing semantic graph structure from raw text are cumbersome and inefficient for long-text documents.
Approach:	They propose a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization.
Outcome:	The proposed model performs state-of-the-art on single- and multi-document summarization tasks while using less memory and fewer parameters.

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Yibo Wang, Xiangjue Dong, James Caverlee, Philip Yu

Robustness in NLP

Challenge:	Recent attacks have shown that adversarial examples have a different data distribution than the original examples, reducing their effectiveness under detection methods.
Approach:	They propose a distribution-aware adversarial attack method that considers the distribution shifts of adversarials to improve attacks’ effectiveness under detection methods.
Outcome:	The proposed method improves the effectiveness of adversarial examples under detection methods and integrates both ASR and detectability.

Evaluating Robustness of Generative Search Engine on Adversarial Factoid Questions (2024.findings-acl)

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Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip Yu, Zhijiang Guo

Robustness in NLP

Challenge:	Existing large language models (LLMs)-backed generative search engines may not always be accurate.
Approach:	They propose to evaluate the robustness of retrieval-augmented generation in a realistic and high-risk setting where adversaries have only black-box system access.
Outcome:	The proposed model exhibits higher susceptibility to factual errors compared to LLMs without retrieval.

Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System (2021.naacl-main)

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Congying Xia, Wenpeng Yin, Yihao Feng, Philip Yu

Low-Resource NLP

Challenge:	Text classification is usually studied by labeling texts with relevant categories from a predefined set.
Approach:	They propose a task where a system incrementally handles multiple rounds of new classes . they propose two entailment approaches, ENTAILMENT and HYBRID, which show promise .
Outcome:	The proposed task is based on a few-shot text classification task in the NLP domain.

Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection (2020.findings-emnlp)

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Hoang Nguyen, Chenwei Zhang, Congying Xia, Philip Yu

Understanding Pre-trained BERT for Aspect-based Sentiment Analysis (2020.coling-main)

Challenge:	Recent studies show that multi-level matching is difficult due to the scarcity of available annotated utterances.
Approach:	They propose a method where semantic components are distilled from utterances via multi-head self-attention with additional dynamic regularization constraints.
Outcome:	The proposed method improves representations of labeled and unlabeled instances while retaining high-level information.

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Hu Xu, Lei Shu, Philip Yu, Bing Liu

Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation (2020.coling-main)

Challenge:	Recent studies show impressive results on aspects-based sentiment analysis tasks.
Approach:	They analyze the attentions and learned representations of BERT for aspects-based sentiment analysis tasks.
Outcome:	The proposed model can be used for aspects-based sentiment analysis (ABSA) but it is not clear how it can provide important features for downstream tasks.

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Zhongfen Deng, Hao Peng, Congying Xia, Jianxin Li, Lifang He, Philip Yu

Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning (2026.acl-long)

Challenge:	Existing methods for review rating prediction ignore hierarchies among data . paper review rating predictions are important for improving paper review process .
Approach:	They propose a Hierarchical bi-directional self-attention Network framework for paper review rating prediction and recommendation . they leverage hierarchical structure of paper reviews with three levels of encoders .
Outcome:	The proposed approach can be used to make an effective decision-making tool for the academic paper review process.

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Zelin Tan, Hejia Geng, Xiaohang Yu, Mulei Zhang, Guancheng Wan, Yifan Zhou, Qiang He, Xiangyuan Xue, Heng Zhou, Yutao Fan, Zhong-Zhi Li, Zaibin Zhang, Guibin Zhang, Chen Zhang, Zhenfei Yin, Philip Torr, Lei Bai

Reasoning

Challenge:	elucidating scaling laws for large language models (LLMs) during pre-training remains unexplored.
Approach:	They characterize how model scale, data, and compute interact during pre-training . they find that large models consistently demonstrate superior compute and data efficiency .
Outcome:	The proposed scaling laws offer practical guidance for scaling reasoning capabilities through reinforcement learning post-training.

Behavioral Consistency Validation for LLM Agents: An Analysis of Trading-Style Switching through Stock-Market Simulation (2026.findings-acl)

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Zeping Li, Guancheng Wan, Keyang Chen, Yu Chen, Yiwen Zhao, Philip Torr, Guangnan Ye, Zhenfei Yin, Hongfeng Chai

Linguistics & Cognitive NLP

Challenge:	Recent studies have applied Large Language Models (LLMs) as agents in financial stock market simulations to test if micro-level behaviors aggregate into macro-level phenomena.
Approach:	They propose four alignment metrics and use Mann–Whitney U tests to compare agents’ style-switching behavior with financial theory.
Outcome:	The proposed model is only partially consistent with financial theory.

Zero-shot User Intent Detection via Capsule Neural Networks (D18-1)

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Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip Yu

MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing (2020.coling-main)

Challenge:	Existing methods to classify intents are labor-intensive and time-consuming as intents will be diverse and new intents may be involved.
Approach:	They propose a zero-shot intent detection problem which aims to detect emerging user intents where no labeled utterances are currently available.
Outcome:	The proposed model can discriminate emerging intents when no labeled utterances are available in training data.

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Tao Zhang, Congying Xia, Chun-Ta Lu, Philip Yu

CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks (2023.emnlp-main)

Challenge:	Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types.
Approach:	They propose a memory-augmented FNET model to tackle unseen types in a zero-shot manner.
Outcome:	The proposed model outperforms the state-of-the-art models with up to 8% gain in Micro-F1 and Macro-F1.

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Hoang Nguyen, Ye Liu, Chenwei Zhang, Tao Zhang, Philip Yu

Multi-grained Named Entity Recognition (P19-1)

Challenge:	Chain-of-Thought prompting is popular in reasoning tasks, but its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored.
Approach:	They propose a Coarse-to-Fine Chain-of-Thought approach that breaks down NLU tasks into multiple reasoning steps where LLMs can learn to acquire essential concepts.
Outcome:	The proposed approach is effective in assisting the LLMs adapt to multi-grained NLU tasks under zero-shot and few-shot multi-domain settings.

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Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu