Papers by Chunyan Miao
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)
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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao
| Challenge: | ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge. |
| Approach: | They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks . |
| Outcome: | ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering. |
Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection (2021.naacl-main)
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| Challenge: | Existing datasets for sarcasm detection are limited due to the difficulty in acquiring ground-truth annotations. |
| Approach: | They propose a generalized latent optimization strategy that allows different losses to accommodate each other and improves training dynamics. |
| Outcome: | The proposed approach outperforms transfer learning and meta-learning baselines and achieves 10.02% performance gain on the iSarcasm dataset. |
Can ChatGPT Assess Human Personalities? A General Evaluation Framework (2023.findings-emnlp)
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| Challenge: | Existing studies study the virtual personalities of LLMs but rarely explore the possibility of analyzing human personalities via LLM. |
| Approach: | They propose to use Myers–Briggs Type Indicator (MBTI) tests to generate unbiased prompts and replace the subject in question statements to enable flexible queries and assessments. |
| Outcome: | The proposed framework enables LLMs to flexibly assess personalities of different groups of people. |
RevMUX: Data Multiplexing with Reversible Adapters for Efficient LLM Batch Inference (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have brought a great breakthrough to the natural language processing community, but their high throughput demands make them difficult to handle concurrent queries. |
| Approach: | They propose a parameter-efficient data multiplexing framework that integrates a reversible design in the multiplexer and can be reused to perform reverse operations and restore individual samples for classification. |
| Outcome: | The proposed framework improves inference efficiency while maintaining satisfactory classification performance. |
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning (2024.acl-long)
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| Challenge: | Recent research shows that training with CAD may lead models to overly focus on modified features while ignoring other important contextual information. |
| Approach: | They propose to use contrastive learning to promote global feature alignment and learning counterfactual clues to improve model performance. |
| Outcome: | The proposed method outperforms the state-of-the-art on out-of distribution (OOD) datasets. |
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER (2021.acl-long)
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| Challenge: | Existing approaches to cross-lingual NER are labeled sequence translation and instance-based transfer via machine translation (MT) Existing methods to cross NER include label projection and labeling, but they are expensive and time-consuming. |
| Approach: | They propose a simple but effective labeled sequence translation method to translate source-language training data to target languages and avoids word order change and entity span determination. |
| Outcome: | The proposed method avoids word order change and entity span determination and can be generalized with the language-specific features from the target-language synthetic data and the language independent features from multilingual synthetic data. |
ConNER: Consistency Training for Cross-lingual Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing consistency training methods for named entity recognition (NER) are likely to violate the consistency hypothesis or focus on coarse-grain consistency. |
| Approach: | They propose a consistency training framework for cross-lingual named entity recognition that leverages unlabeled target-language data and dropout-based consistency training on labeled source-language datasets. |
| Outcome: | The proposed framework improves on translation-based consistency training on unlabeled target-language data and dropout-based consistent training on labeled source-language datasets. |
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)
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| Challenge: | Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance. |
| Approach: | They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities. |
| Outcome: | The proposed framework outperforms baseline methods on low-resource tasks. |
History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System (2022.findings-emnlp)
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| Challenge: | Existing open-domain dialogue systems conduct one-session conversations, but multi-session MSCs are under-investigated. |
| Approach: | They propose a History-Aware Hierarchical Transformer for multi-session open-domain dialogue . they propose to encode history conversations into a history memory and leverage historical information to generate well-informed responses. |
| Outcome: | The proposed model outperforms baseline models on a large-scale MSC dataset. |
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (D19-1)
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| Challenge: | Existing methods to analyze emotions in textual conversations are limited . emotion detection is challenging because humans rely on context and commonsense knowledge to express emotions . |
| Approach: | They propose a Knowledge-Enriched Transformer where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged. |
| Outcome: | The proposed model outperforms state-of-the-art models on most of the tested datasets in F1 score. |
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks (2020.emnlp-main)
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Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, Chunyan Miao
| Challenge: | Data augmentation techniques are widely used to improve machine learning performance . however, due to the complexity of language, it is difficult to generalize such rules for languages. |
| Approach: | They propose a method to generate high quality synthetic data for low-resource tagging tasks . they use unlabeled data only and unlabelled data plus a knowledge base . |
| Outcome: | The proposed method outperforms baselines on NER, part of speech and target based sentiment analysis tasks. |
Exploring Representation-level Augmentation for Code Search (2022.emnlp-main)
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| Challenge: | Recent data augmentations for code search are at the raw-data level, which requires additional code analysis and training cost. |
| Approach: | They propose a general format of representation-level augmentation that unifies existing methods. |
| Outcome: | The proposed methods can boost the performance of code search models on a large-scale dataset. |
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning (2023.acl-long)
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| Challenge: | Existing methods to bridge the linguistic gap between self-training and monolingual named entity recognition (NER) however, due to sub-optimal performance on target languages, the pseudo labels are noisy and limit the overall performance. |
| Approach: | They propose to combine representation learning and pseudo label refinement in one coherent framework to improve self-training for cross-lingual named entity recognition (NER) |
| Outcome: | The proposed method improves cross-lingual named entity recognition (NER) on multiple transfer pairs. |
Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in multimodal reasoning overlook the audio modality. |
| Approach: | They propose a large-scale audio language model for deep reasoning that leverages a multitask audio dataset. |
| Outcome: | The proposed model performs well across key benchmarks including MMAU-mini, AIR-Bench chat/foundation, and MELD. |
Towards Persona-Based Empathetic Conversational Models (2020.emnlp-main)
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| Challenge: | Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. |
| Approach: | They propose a task towards persona-based empathetic conversations and propose e-learning model CoBERT that can be used to train persona on emmpathetic conversations. |
| Outcome: | The proposed model improves empathetic responding more when trained on e-mpathetic conversations than non-empathy ones. |
SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs (2025.acl-long)
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| Challenge: | Existing approaches to continuous-space reasoning focus on hard token decoding and suffer from catastrophic forgetting. |
| Approach: | They propose a method that generates instance-specific soft thought tokens as the initial chain of thoughts and maps them into the LLM’s representation space via a trainable projection module. |
| Outcome: | The proposed method improves LLM reasoning performance through supervised, parameter-efficient fine-tuning. |
Rethinking Negative Pairs in Code Search (2023.emnlp-main)
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| Challenge: | Comparative learning is a key component in fine-tuning code search models . however, negative samples of InfoNCE may deteriorate its representation learning . |
| Approach: | They propose a loss function that inserts weight terms into InfoNCE to improve contrastive learning. |
| Outcome: | The proposed loss function is a special case of Soft-InfoNCE, the authors show . it is more accurate than other loss functions, and it is faster than other models. |
GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems (2022.acl-long)
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| Challenge: | Existing multilingual task-oriented dialogue datasets lack high-quality data curation due to the high expense and challenges of human annotation. |
| Approach: | They propose a method that generates a multilingual ToD dataset globalized from an English ToD data set for three unexplored use cases of multilingual toD systems. |
| Outcome: | The proposed method generates a large-scale multilingual ToD dataset globalized from an English ToD data set for three unexplored use cases of multilingual toD systems. |