Papers by Lidia S. Chao

Shared-Private Bilingual Word Embeddings for Neural Machine Translation (P19-1)

Challenge:	Existing models for multi-domain translation tasks only use monolingual data, whereas bilingual data is indispensable for improving the models.
Approach:	They propose a modular strategy that facilitates the cooperation of monolingual and bilingual knowledge in translation tasks by avoiding catastrophic forgetting.
Outcome:	The proposed model exhibits superior generalization and robustness over the conventional approach.

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Xuebo Liu, Derek F. Wong, Yang Liu, Lidia S. Chao, Tong Xiao, Jingbo Zhu

Norm-Based Curriculum Learning for Neural Machine Translation (2020.acl-main)

Challenge:	Word embedding is central to neural machine translation, but indirectly interfaces with other layers, making them comparatively isolated.
Approach:	They propose a shared-private bilingual word embedding which gives a closer relationship between the source and target embedders and reduces the number of model parameters.
Outcome:	The proposed model improves on 5 language pairs belonging to 6 different language families and written in 5 different alphabets and significantly reduces model parameters.

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Xuebo Liu, Houtim Lai, Derek F. Wong, Lidia S. Chao

Difficulty-Aware Machine Translation Evaluation (2021.acl-short)

Challenge:	Experimental results show that the proposed method outperforms strong baselines in terms of BLEU score (+1.17/+1.56) and training speedup (2.22x/3.33x).
Approach:	They propose a norm-based curriculum learning method that measures difficulty, competence and weight of a sentence in a word embedding.
Outcome:	The proposed method outperforms baselines in terms of BLEU score (+1.17/+1.56) and training speedup (2.22x/3.33x).

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Runzhe Zhan, Xuebo Liu, Derek F. Wong, Lidia S. Chao

Modeling Localness for Self-Attention Networks (D18-1)

Challenge:	Current MT evaluation measures pay the same attention to each sentence component . in real-world examinations, the questions vary in difficulty and weightings .
Approach:	They propose a difficulty-aware MT evaluation metric that takes translation difficulty into account . they propose to use this metric to evaluate machine translation (MT) results .
Outcome:	The proposed method outperforms most MT evaluation metrics in terms of human correlation.

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Baosong Yang, Zhaopeng Tu, Derek F. Wong, Fandong Meng, Lidia S. Chao, Tong Zhang

Let’s Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model (2025.coling-main)

Challenge:	Existing approaches to model locality for self-attention networks have shown great value for capturing global dependencies.
Approach:	They propose to model localness for self-attention networks to capture local context . they cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention.
Outcome:	The proposed model improves the ability to capture local context and improves accuracy.

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Haoyun Xu, Runzhe Zhan, Yingpeng Ma, Derek F. Wong, Lidia S. Chao

Intrinsic Model Weaknesses: How Priming Attacks Unveil Vulnerabilities in Large Language Models (2025.findings-naacl)

Challenge:	Large Language Models (LLMs) are composed of neurons that exhibit diverse behaviors and roles.
Approach:	They propose a novel approach that refines the granularity of parameter training down to the individual neuron, enabling a more parameter-efficient fine-tuning model.
Outcome:	The proposed approach exceeds the performance of full-parameter fine-tuning and PEFT and provides insights into the analysis of neurons.

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Yuyi Huang, Runzhe Zhan, Derek F. Wong, Lidia S. Chao, Ailin Tao

From Scenes to Elements: Multi-Granularity Evidence Retrieval for Verifiable Multimodal RAG (2026.findings-acl)

Challenge:	Large language models (LLMs) have significant impact on various industries and societal functions due to advanced instruction-following capabilities.
Approach:	They developed and tested novel attack strategies on popular LLMs to expose their vulnerabilities in generating harmful content.
Outcome:	The proposed attacks achieved an ASR of 100% on open-source models, including Meta’s Llama-3.2, Google’s Gemma-2, Mistral’s Mistral-NeMo, Falcon’s Falcon-mamba, Apple’s DCLM, Microsoft’s Phi3, and Qwen’s Qwend2.5, among others.

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Guanhua Chen, Chuyue Huang, Yutong Yao, Shudong Liu, Xueqing Song, Lidia S. Chao, Derek F. Wong

Multimodality

Challenge:	Existing multimodal Retrieval-Augmented Generation (RAG) systems retrieve evidence at coarse granularities, making failures unverifiable.
Approach:	They propose a multimodal benchmark that features real-world landmarks with annotations across multiple viewpoints and a framework that treats visual elements as first-class retrieval units through three stages: element-level detection and classification, multi-granularity cross-modal alignment for evidence retrieval, and attribution-constrained generation.
Outcome:	The proposed framework achieves up to 29.2% improvement over six strong baselines for this task.

Assessing the Ability of Self-Attention Networks to Learn Word Order (P19-1)

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Baosong Yang, Longyue Wang, Derek F. Wong, Lidia S. Chao, Zhaopeng Tu

Poller: Are LLMs Suitable for Evaluating Poetry Understanding Task? (2026.findings-acl)

Challenge:	Existing studies have attributed SAN to being weak at learning positional information for sequence modeling due to lack of recurrence structure.
Approach:	They propose a word reordering detection task to quantify how well word order information is learned by SAN and RNN.
Outcome:	The proposed task quantifies how well word order information learned by SAN and RNN is learned.

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Shanshan Wang, Derek F. Wong, Jingming Yao, Lidia S. Chao

Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore (2025.coling-main)

Challenge:	Traditional methods for poetry evaluation are expensive and unsuitable for large-scale data.
Approach:	They propose a method leveraging Large Language Models to evaluate poetry understanding tasks using Large Language models.
Outcome:	The proposed method reduces the evaluation error between LLMs and humans by adopting the poet's perspective.

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Junchao Wu, Runzhe Zhan, Derek F. Wong, Shu Yang, Xuebo Liu, Lidia S. Chao, Min Zhang

Low-Resource NLP

Challenge:	Existing methods for detecting LLM-generated text require no training data.
Approach:	They propose a black-box zero-shot detection approach that calculates the Grammar Error Correction Score for a given text to differentiate between human-written and LLM-generated texts.
Outcome:	The proposed method outperforms current state-of-the-art zero-shot and supervised methods, achieving an average AUROC of 98.62% across XSum and Writing Prompts datasets.

Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing (2026.acl-long)

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Fengying Ye, Shanshan Wang, Lidia S. Chao, Derek F. Wong

Sentiment Analysis

Challenge:	Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such success actually reveals about metaphor processing.
Approach:	They propose to probing semantic attribute alignment, lexical invariance, and syntactic sensitivity to examine the limits of behavioral evidence for metaphor processing.
Outcome:	The proposed model can exhibit semantic drift relative to reference attributes, stable lexical anchors persist across contextual conditions, potentially supporting conventional metaphors while biasing novel metaphors requiring contextual integration.

VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning (2026.acl-long)

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Jingkun Ma, Runzhe Zhan, Yang Li, Di Sun, Hou Pong Chan, Lidia S. Chao, Derek F. Wong

Reasoning

Challenge:	Existing Large Multi-modal Models lack a robust visual processing capability that is often masked by evaluation metrics that prioritize final-answer accuracy.
Approach:	They propose a three-layer evaluation framework that scrutinizes the generation of valid visual aids and the soundness of subsequent reasoning steps.
Outcome:	The proposed framework examines the generation of valid visual aids and the soundness of subsequent reasoning steps on state-of-the-art models.

TransGEC: Improving Grammatical Error Correction with Translationese (2023.findings-acl)

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Tao Fang, Xuebo Liu, Derek F. Wong, Runzhe Zhan, Liang Ding, Lidia S. Chao, Dacheng Tao, Min Zhang

A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU (2024.lrec-main)

Challenge:	Experimental results show that data augmentation improves accuracy over strong baselines.
Approach:	They propose to use translationese as input for GEC data augmentation to overcome stylistic discrepancies . they propose to obtain human-translated texts with a more similar style to non-native texts .
Outcome:	The proposed method improves correction accuracy over strong baselines on four GEC benchmarks.

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Guanhua Chen, Yutong Yao, Derek F. Wong, Lidia S. Chao

Can ChatGPT Really Understand Modern Chinese Poetry? (2026.findings-eacl)

Challenge:	Multi-intent natural language understanding (NLU) models lack the rich information between the shared intents, especially in low-data scenarios.
Approach:	They propose a two-stage framework for multi-intent natural language understanding to harness shared intent information by word-level pre-training and prediction-aware contrastive fine-tuning.
Outcome:	The proposed framework surpasses baselines on low-data and full-data scenarios.

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Shanshan Wang, Derek F. Wong, Jingming Yao, Lidia S. Chao

Dialogue Systems & Conversational Agents

Challenge:	Recent studies have focused on poetry generation and translation, but their scope has been limited to evaluation and analysis of experimental results without addressing fundamental issues of comprehension.
Approach:	They propose a framework for evaluating ChatGPT's understanding of modern poetry . they evaluated the interpretations of unpublished modern Chinese poems by different poets .
Outcome:	The proposed framework is based on the evaluation of unpublished poems by poets and shows that its interpretations align with the original poets’ intents in over 73% of the cases.

Path Drift in Large Reasoning Models: How First-Person Commitments Override Safety (2025.emnlp-main)

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Yuyi Huang, Runzhe Zhan, Lidia S. Chao, Ailin Tao, Derek F. Wong

Reasoning

Challenge:	Existing studies on prompt injection and jailbreak attacks primarily target the surface structure of input prompts.
Approach:	They propose a three-stage approach to mitigate the risk of Long-CoT reasoning drift . they propose 'path-level defense' strategy that incorporates role attribution correction and metacognitive reflection .
Outcome:	The proposed framework reduces refusal rates and ethical evaporation, while ethical escalation and layered disclaimers progressively steer models toward unsafe completions.

ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation (2022.emnlp-main)

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Zhaocong Li, Xuebo Liu, Derek F. Wong, Lidia S. Chao, Min Zhang

GuoFeng: A Benchmark for Zero Pronoun Recovery and Translation (2022.emnlp-main)

Challenge:	Existing transfer learning methods for low-resource NMT are static, which simply transfer knowledge from a parent model to a child model once via parameter initialization.
Approach:	They propose a transfer learning method that can continuously transfer knowledge from the parent model during the training of the child model.
Outcome:	The proposed method can transfer knowledge from the parent model to the child model during the training of the child.

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Mingzhou Xu, Longyue Wang, Derek F. Wong, Hongye Liu, Linfeng Song, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

On the Copying Behaviors of Pre-Training for Neural Machine Translation (2021.findings-acl)

Challenge:	ZPs are often omitted when they can be pragmatically or grammatically inferred from intraand inter-sentential contexts.
Approach:	They propose a benchmark testset for target evaluation on Chinese-English ZP translation.
Outcome:	The proposed testset covers five genres and identifies current challenges for evaluation.

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Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

Self-Paced Learning for Neural Machine Translation (2020.emnlp-main)

Challenge:	Existing studies show that initializing NMT models with pre-trained language models (LM) can speed up the model training and boost the model performance.
Approach:	They propose a method to control copying behaviors in NMT models by initializing them with pre-trained language models (LM) they propose to use a metric called copy ratio to control the copying behavior in decoding.
Outcome:	The proposed method improves translation performance by controlling copying behaviors for pre-training based models.

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Yu Wan, Baosong Yang, Derek F. Wong, Yikai Zhou, Lidia S. Chao, Haibo Zhang, Boxing Chen

On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (2021.findings-emnlp)

Challenge:	Existing studies have shown that the training of neural machine translation (NMT) rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word rarity.
Approach:	They propose to train NMT model using a self-paced learning approach that allows it to quantify the learning confidence over training examples and flexibly govern its learning via regulating the loss in each iteration step.
Outcome:	The proposed model outperforms baseline models and those trained with human-designed curricula on translation quality and convergence speed.

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Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

Rethinking Prompt-based Debiasing in Large Language Model (2025.findings-acl)

Challenge:	Experimental results show that PT and BT are nicely complementary to each other.
Approach:	They introduce two probing tasks for PT and BT respectively and investigate their complementarity.
Outcome:	The proposed methods establish state-of-the-art on the WMT16 English-Romanian and English-Russian benchmarks.

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Xinyi Yang, Runzhe Zhan, Shu Yang, Junchao Wu, Lidia S. Chao, Derek F. Wong

Responsible & Trustworthy NLP

Challenge:	Existing prompt-based methods for debiasing are often superficial and lack a thorough understanding of complex bias concepts.
Approach:	They analyze a BBQ and stereoSet benchmarks to examine the assumption that large language models understand biases.
Outcome:	The proposed model misclassified 90% of unbiased content as biased despite high accuracy on BBQ dataset . the proposed model may have been flawed in previous attempts to debiase .

SGIC: A Self-Guided Iterative Calibration Framework for RAG (2025.acl-long)

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Guanhua Chen, Yutong Yao, Lidia S. Chao, Xuebo Liu, Derek F. Wong

Information Retrieval

Challenge:	Existing studies on retrieval-augmented generation (RAG) focus on extracting relevant documents or refinement of specialized instructions.
Approach:	They propose a framework that provides LLMs with specific cues to improve their calibration efficacy . they propose an iterative self-calibration training set that harnesses uncertainty scores .
Outcome:	The proposed framework significantly improves performance on both closed-source and open-source LLMs.

Document Graph for Neural Machine Translation (2021.emnlp-main)

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Mingzhou Xu, Liangyou Li, Derek F. Wong, Qun Liu, Lidia S. Chao

kNN-TL: k-Nearest-Neighbor Transfer Learning for Low-Resource Neural Machine Translation (2023.acl-long)

Challenge:	Existing document-level NMT methods fail to leverage contexts beyond a few set of previous sentences.
Approach:	They propose to represent a document as a graph that connects relevant contexts regardless of distances.
Outcome:	Experiments on IWSLT English–French, Chinese-English, WMT English–German and Opensubtitle English–Russian show that using document graphs can significantly improve translation quality.

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Shudong Liu, Xuebo Liu, Derek F. Wong, Zhaocong Li, Wenxiang Jiao, Lidia S. Chao, Min Zhang

Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA (2026.findings-acl)

Challenge:	Transfer learning is an effective technique for enhancing low-resource neural machine translation (NMT) however, these methods do not make use of the parent knowledge during the child inference, which may limit the translation performance.
Approach:	They propose a k-Nearest-Neighbor Transfer Learning approach which leverages the parent knowledge throughout the entire developing process of the child model.
Outcome:	The proposed approach outperforms strong baselines on four low-resource translation tasks.

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Guanhua Chen, Yutong Yao, Shenghe Sun, Ci-jun Gao, Shudong Liu, Lidia S. Chao, Feng Wan, Derek F. Wong

Multimodality

Challenge:	Recent advances in vision-language models (VLMs) have achieved impressive results on standard image-text tasks, yet their capability in visual procedure question answering (VP-QA) remains largely unexplored.
Approach:	They propose a multimodal benchmark specifically designed for visual procedural reasoning that synergizes cross-modal procedure retrieval, context-aware step decomposition, and the next step prediction.
Outcome:	The proposed framework significantly outperforms baselines on visual procedure question answering (VP-QA) Experiments on six VLMs show that it performs better than baselines.

3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset (2024.lrec-main)

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Xinyu Ma, Xuebo Liu, Derek F. Wong, Jun Rao, Bei Li, Liang Ding, Lidia S. Chao, Dacheng Tao, Min Zhang

Uncertainty-Aware Curriculum Learning for Neural Machine Translation (2020.acl-main)

Challenge:	Existing studies have shown that visual information in existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities.
Approach:	They propose to use 3AM to create an ambiguity-aware multimodal machine translation dataset.
Outcome:	The proposed dataset includes more ambiguity and a greater variety of captions and images than other MMT datasets.

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Yikai Zhou, Baosong Yang, Derek F. Wong, Yu Wan, Lidia S. Chao

Convolutional Self-Attention Networks (N19-1)

Challenge:	Neural machine translation (NMT) has proven to be facilitated by curriculum learning which presents examples in an easy-to-hard order at different training stages.
Approach:	They propose to use an uncertainty-aware curriculum learning approach to assess data difficulty and model competence to provide examples in an easy-to-hard order at different training stages.
Outcome:	The proposed approach outperforms baseline and related methods on translation quality and convergence speed.

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Baosong Yang, Longyue Wang, Derek F. Wong, Lidia S. Chao, Zhaopeng Tu

Learning Deep Transformer Models for Machine Translation (P19-1)

Challenge:	Existing models of self-attention networks lack the ability to capture dependencies regardless of distance and can be enhanced with multi-head attention.
Approach:	They propose a convolutional self-attention network which can be enhanced by multi-head attention by allowing the model to attend to information from different representation subspaces.
Outcome:	The proposed model outperforms existing models on improving locality of SANs on different language pairs and model settings.

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Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, Lidia S. Chao

G-IdiomAlign: A Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment (2026.acl-long)

Challenge:	Neural machine translation models have advanced the previous state-of-the-art by learning mappings between sequences via neural networks and attention mechanisms.
Approach:	They propose to use layer normalization to pass the combination of previous layers to the next layer to improve the model.
Outcome:	The proposed model outperforms the shallow Transformer-Big/Base baseline model on English-German and Chinese-English tasks by 0.4-2.4 BLEU points.

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Fengying Ye, Yanming Sun, Runzhe Zhan, Lidia S. Chao, Zheqi Zhang, Derek F. Wong

Improving Grammatical Error Correction with Multimodal Feature Integration (2023.findings-acl)

Challenge:	Existing tools for cross-lingual idiom-to-idiom equivalence evaluation are limited . figurative meanings are non-compositional and culturally grounded, making literal mappings unreliable.
Approach:	They propose a gloss-pivoted benchmark where each idiom is anchored by an English gloss from Wiktionary.
Outcome:	The proposed benchmark is based on a dictionary-anchored English idiom . a bias to literal translation is a dominant failure mode across diverse LLMs, the study shows .

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Tao Fang, Jinpeng Hu, Derek F. Wong, Xiang Wan, Lidia S. Chao, Tsung-Hui Chang

Multimodality

Challenge:	Experimental results show that multimodal GEC models improve over strong baselines and achieve a new state-of-the-art result on the Falko-MERLIN test set.
Approach:	They propose a framework that integrates both speech and text features to enhance GEC by generating audio from text using advanced text-to-speech models.
Outcome:	The proposed framework improves on CoNLL14, BEA19 English, and Falko-MERLIN German datasets.

Test-time Adaptation for Machine Translation Evaluation by Uncertainty Minimization (2023.acl-long)

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Runzhe Zhan, Xuebo Liu, Derek F. Wong, Cuilian Zhang, Lidia S. Chao, Min Zhang