Papers by Dit-Yan Yeung
Multilingual and Multi-Aspect Hate Speech Analysis (D19-1)
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| Challenge: | Current research on hate speech analysis is oriented towards monolingual and single classification tasks. |
| Approach: | They propose to use a multilingual multi-aspect hate speech analysis dataset to test current methods . they evaluate the dataset in various classification settings and discuss how to leverage annotations . |
| Outcome: | The proposed dataset can be used to improve hate speech detection and classification in general. |
Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets (2020.emnlp-main)
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| Challenge: | a recent study has shown that data collection is neglected by ignoring the quality of data. |
| Approach: | They propose to use latent semantics to evaluate selection bias in hate speech . they compare latent Dirichlet Allocation (LDA) to eleven hate speech corpora . |
| Outcome: | The proposed method could be revisable before focusing on classification performance. |
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders (2022.findings-acl)
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| Challenge: | Variational autoencoders (VAEs) have been widely applied in text generation tasks, but they suffer from insufficient representation capacity and poor controllability. |
| Approach: | They propose a data-driven prior that has expressivity and controllability. |
| Outcome: | The proposed prior enjoys expressivity and controllability and can be used in language modeling and controlled text generation. |
Towards Reference-free Text Simplification Evaluation with a BERT Siamese Network Architecture (2023.findings-acl)
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| Challenge: | Text simplification (TS) aims to modify sentences to make their content and structure easier to understand. |
| Approach: | They propose a neural-network-based TS metric that uses a human reference to evaluate simplification and meaning preservation. |
| Outcome: | The proposed metric correlates better with human judgments for simplicity and meaning preservation than existing metrics. |
SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme (2023.findings-acl)
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| Challenge: | Existing methods of generating singable lyrics are based on a given melody, but there are two main challenges: generating the lyrics without knowing the melody and composing compatible melodies. |
| Approach: | They propose a Chinese lyric generation and editing system which rewrites lyrics of an existing song such that they are compatible with the rhythm of the existing melody. |
| Outcome: | The proposed system is based on a randomized multi-level masking strategy and can generate new lyrics or edit fragments without prior knowledge of melody composition. |
Probing Toxic Content in Large Pre-Trained Language Models (2021.acl-long)
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| Challenge: | Existing studies on pre-trained language models have shown that they carry harmful biases towards different social groups. |
| Approach: | They propose a method to probe English, French, and Arabic PTLMs and quantify the potentially harmful content they convey with respect to a set of templates. |
| Outcome: | The proposed method analyzes PTLMs to predict masked tokens at the end of sentences to assess their toxicity. |
The Stochastic Parrot on LLM’s Shoulder: A Summative Assessment of Physical Concept Understanding (2025.naacl-long)
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| Challenge: | Recent years have witnessed remarkable advancements in large language models (LLMs) many researchers argue that LLMs may not * Equal contribution. |
| Approach: | They propose a task that summarises the memorization issue by using grid inputs that abstractly describe physical phenomena. |
| Outcome: | The proposed task alleviates the memorization issue by using grid-format inputs that abstractly describe physical phenomena. |
DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing logic reasoning benchmarks are limited in language diversity and their distributions are deviated from ideal distributions, which may lead to biased evaluation results. |
| Approach: | They propose a new logic benchmark DivLogicEval that uses natural sentences to evaluate logical reasoning . |
| Outcome: | The proposed evaluation metric mitigates bias and randomness inherent in LLMs. |
Understanding LLMs’ Fluid Intelligence Deficiency: An Analysis of the ARC Task (2025.naacl-long)
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| Challenge: | Recent research on fluid intelligence assessments has highlighted significant deficiencies in LLMs’ abilities. |
| Approach: | They analyze the challenges LLMs face in demonstrating fluid intelligence through controlled experiments using the most representative ARC task as an example. |
| Outcome: | The proposed model shows that it lacks the ability to combine skill composition and abstract input formats and lacks left-to-right decoding. |
Towards General Error Diagnosis via Behavioral Testing in Machine Translation (2023.findings-emnlp)
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| Challenge: | Existing behavioral testing approaches only evaluate translation quality without references, restricting diagnosis to specific types of errors. |
| Approach: | They propose a bilingual translation pair generation based behavior testing framework that auto-generates test cases and pseudo-references to facilitate general error diagnosis. |
| Outcome: | The proposed framework can provide comprehensive and accurate behavioral testing results for general error diagnosis on machine translation systems. |
Selection-p: Self-Supervised Task-Agnostic Prompt Compression for Faithfulness and Transferability (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities when leveraging in-context learning. |
| Approach: | They propose a method that discretizes uninformative tokens using a self-supervised pre-training technique. |
| Outcome: | The proposed method achieves state-of-the-art performance across classification tasks while requiring only 0.8% decrease in performance. |
Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models (2025.acl-long)
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| Challenge: | Long-context language models have impressive capabilities in long-contrast understanding tasks, but long-text referencing remains underexplored. |
| Approach: | They propose a benchmark to assess long-context referencing capability of LCLMs . they use three subsets to test the model's ability to identify key indexes based on contextual relationships . |
| Outcome: | The proposed benchmark assesses the long-context referencing capability of LCLMs. |
Situated Embedding Models for Context-Aware Dense Retrieval (2026.acl-short)
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| Challenge: | Existing embedding models are not well-equipped to encode situated context effectively, i.e., situating a chunk’s meaning within its context. |
| Approach: | They propose to represent short chunks in a way that is conditioned on a broader context window to enhance retrieval performance. |
| Outcome: | The proposed model outperforms state-of-the-art embedding models on a book-plot retrieval dataset. |