Papers by Simon See

Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport (2023.findings-acl)

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Zihao Wang, Weizhi Fei, Hang Yin, Yangqiu Song, Ginny Wong, Simon See

Semantic Text Processing

Challenge:	Existing methods for answering complex queries on knowledge graphs lack a local and global scoring function.
Approach:	They propose a convolution-based algorithm for linear time computation and a block diagonal kernel to enforce the trade-off between local and global embeddings.
Outcome:	The proposed model outperforms existing methods on standard datasets, evaluation sets with combinatorially complex queries, and hierarchical knowledge graphs.

LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoning (2025.emnlp-main)

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Tianshi Zheng, Cheng Jiayang, Chunyang Li, Haochen Shi, Zihao Wang, Jiaxin Bai, Yangqiu Song, Ginny Wong, Simon See

Multimodality

Challenge:	Modern large language models (LLMs) employ diverse logical inference mechanisms for reasoning.
Approach:	They analyze the comparative dynamics of inductive (System 1) versus abductive/deductive (system 2) inference in large language models by using a controlled analogical reasoning environment and a MCQ/free-text task format.
Outcome:	The proposed methods can significantly scale LLM reasoning.

XToM: Exploring the Multilingual Theory of Mind for Large Language Models (2026.acl-long)

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Chunkit Chan, Yauwai Yim, Hongchuan Zeng, Zhiying Zou, Xinyuan Cheng, Zhifan Sun, Zheye Deng, Kawai Chung, Yuzhuo Ao, Fan Yixiang, Cheng Jiayang, Ercong Nie, Ginny Wong, Helmut Schmid, Hinrich Schuetze, Simon See, Yangqiu Song

Semantic Text Processing

Challenge:	Existing evaluations of ToM in LLMs are limited to English, neglecting the linguistic diversity that shapes human cognition.
Approach:	They propose a multilingual benchmark that evaluates ToM across five languages . they find that models excel in multilingual language understanding, but their ToM performance varies across languages.
Outcome:	The proposed benchmark evaluates LLMs across five languages and incorporates diverse task scenarios.

COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective (2023.acl-long)

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Zhaowei Wang, Quyet V. Do, Hongming Zhang, Jiayao Zhang, Weiqi Wang, Tianqing Fang, Yangqiu Song, Ginny Wong, Simon See

Commonsense Reasoning

Challenge:	Existing efforts to detect commonsense causation from the causal inference perspective are inadequate to seize commonsensical causations.
Approach:	They propose a task to detect commonsense causation between two events in context . they propose 'contextualized commons sense causal reasoning' framework that uses covariates to remove confounding effects .
Outcome:	The proposed framework can detect commonsense causality more accurately than baselines.

SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller (2022.emnlp-main)

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Zhaowei Wang, Hongming Zhang, Tianqing Fang, Yangqiu Song, Ginny Wong, Simon See

Text Generation

Challenge:	In this paper, we propose a new task of sub-event generation for an unseen process . we use a framework to generate coherent sub-Event sequences for unseened processes .
Approach:	They propose a task of sub-event generation for an unseen process to evaluate the understanding of the coherence of subevent actions and objects.
Outcome:	The proposed framework can generate coherent sub-event sequences for unseen processes . it can also decode more coherent subevents, demonstrating its effectiveness .

AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation (2024.acl-long)

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Zhaowei Wang, Wei Fan, Qing Zong, Hongming Zhang, Sehyun Choi, Tianqing Fang, Xin Liu, Yangqiu Song, Ginny Wong, Simon See

Explainability & Interpretability in NLP

Challenge:	Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored.
Approach:	They propose a framework AbsInstruct to enhance LLMs’ abstract ability through instruction tuning.
Outcome:	The proposed framework can enhance LLMs’ abstraction ability with strong generalization performance while maintaining their general instruction-following abilities.

Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform (2022.emnlp-main)

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Huiru Xiao, Xin Liu, Yangqiu Song, Ginny Wong, Simon See

Semantic Text Processing

Challenge:	Existing studies have shown that the choice of space for knowledge graph (KG) embeddings has significant effects on the performance of KG completion tasks.
Approach:	They propose to use the Fourier transform to convert between real and complex hyperbolic space to capture hierarchical patterns.
Outcome:	The proposed models outperform the baseline models for knowledge graph (KG) embeddings.

NewsMet : A ‘do it all’ Dataset of Contemporary Metaphors in News Headlines (2023.findings-acl)

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Rohan Joseph, Timothy Liu, Aik Beng Ng, Simon See, Sunny Rai

Sentiment Analysis

Challenge:	Popular datasets used for metaphor processing tasks were constructed from dated source texts.
Approach:	They propose a large contemporary dataset of news headlines hand-annotated with metaphorical verbs.
Outcome:	The proposed dataset includes headlines from political, satirical, reliable and fake sources.

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition (2023.findings-acl)

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Chunkit Chan, Xin Liu, Jiayang Cheng, Zihan Li, Yangqiu Song, Ginny Wong, Simon See

Semantic Text Processing

Challenge:	Existing works on implicit discourse relation recognition focus on syntax features and lack of connectives.
Approach:	They propose a prompt-based path prediction method that integrates the interactive information and intrinsic senses among the hierarchy in IDRR.
Outcome:	The proposed method shows significant improvement against competitive baselines.

IRIS: Interleaved Reinforcement with Incremental Staged Curriculum for Cross-Lingual Mathematical Reasoning (2026.acl-long)

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Navya Gupta, Rishitej Reddy Vyalla, Avinash Anand, Chhavi Kirtani, Erik Cambria, Zhengchen Zhang, Zhengkui Wang, Timothy Liu, Aik Beng Ng, Simon See, Rajiv Ratn Shah

Multilinguality

Challenge:	Curriculum learning fails to generate consistent step-by-step reasoning in multilingual and low-resource settings.
Approach:	They propose a framework that combines supervised fine-tuning with reverse curriculum reinforcement learning to generate consistent step-by-step reasoning.
Outcome:	The proposed framework outperforms single-axis benchmarks and multilingual test sets on math reasoning tasks and in high-resource languages.

PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population (2022.findings-emnlp)

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Tianqing Fang, Quyet V. Do, Hongming Zhang, Yangqiu Song, Ginny Y. Wong, Simon See

Semantic Text Processing

Challenge:	Commonsense Knowledge Base (CSKB) Population aims at reasoning over unseen entities and assertions on CSKBs, but it requires out-of-domain generalization ability as the source CSMB for training is of a relatively smaller scale (1M) .
Approach:	They propose a semi-supervised learning framework that uses a teacher model to provide pseudo labels on the unlabeled candidate dataset for a student model to learn from.
Outcome:	The proposed framework can improve the backbone model KG-BERT (RoBERTa-large) by 3.3 points on the overall performance and especially, 5.3 points on out-of-domain performance.