Papers by Xingwei Tan
Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation? (2025.emnlp-main)
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| Challenge: | Recent work has demonstrated that using chain of thought (CoT) on soft-reasoning tasks can yield limited or even negative performance gains. |
| Approach: | They investigate how chain of thought (CoT) is used in soft-reasoning tasks across instruction-tuned, reasoning and reasoning-distilled models. |
| Outcome: | The proposed model can steer predictions without faithfully reflecting reasoning, indicating a disconnect between CoT influence and faithfulness. |
Extracting Event Temporal Relations via Hyperbolic Geometry (2021.emnlp-main)
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| Challenge: | Recent neural approaches to event temporal relation extraction map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. |
| Approach: | They propose to embed events into hyperbolic spaces to model hierarchical structures . they propose to use hyperbolical embeddings to directly infer event relations . |
| Outcome: | The proposed architecture is based on two approaches to encode events and their temporal relations in hyperbolic spaces. |
IntrEx: A Dataset for Modeling Engagement in Educational Conversations (2025.findings-emnlp)
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| Challenge: | IntrEx is the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
| Approach: | They propose a large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
| Outcome: | The proposed dataset is the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models (2026.findings-acl)
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Mingzi Cao, Xingwei Tan, Mahmud Elahi Akhter, Marco Valentino, Maria Liakata, Xi Wang, Nikolaos Aletras
| Challenge: | Deduction, induction, and abduction are fundamental reasoning paradigms, core for human logical thinking. |
| Approach: | They propose to use a dataset of symbolic tasks to induce deductive skills into large language models (LLMs) they then use FT to fine-tune models to improve OOD generalization . |
| Outcome: | The proposed approach yields strong generalizability with substantial performance gains (up to 14.60) across realistic out-of-domain tasks. |
Event Temporal Relation Extraction with Bayesian Translational Model (2023.eacl-main)
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| Challenge: | Existing methods to extract temporal relations between events lack a principled method to incorporate external knowledge. |
| Approach: | They propose a Bayesian-based method that models the temporal relation representations as latent variables and infers their values via Bayessian inference and translational functions. |
| Outcome: | The proposed method outperforms existing methods for event temporal relation extraction on three widely used datasets. |
Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation (2022.findings-emnlp)
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| Challenge: | Existing QA frameworks that use event-centric reasoning are lacking. |
| Approach: | They propose a novel QA model with contrastive learning and invertible event transformation . they use an invertable transformation matrix to project event vectors into a common event embedding space . |
| Outcome: | The proposed model achieves 8.4% gain in token-level F1 score and 3.0% gain in Exact Match score on the ESTER dataset. |
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks (D19-1)
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| Challenge: | Existing models ignore conflict opinions because they are sparse in the datasets. |
| Approach: | They propose a multi-label classification model with dual attention mechanism to address these problems by excluding conflict opinions from existing models. |
| Outcome: | The proposed model addresses the problem of exclusion of conflict opinions from the datasets. |
Cascading Large Language Models for Salient Event Graph Generation (2025.naacl-long)
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| Challenge: | Existing studies on event graph generation rely on distant supervision for event graphs . |
| Approach: | They propose a CAscading Large Language Model framework for SAlient Event graph generation which leverages the capabilities of LLMs and eliminates the need for costly human annotations. |
| Outcome: | The proposed method outperforms baseline models on a human-annotated test set. |
Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation (2024.naacl-long)
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| Challenge: | Existing methods for constructing event temporal graphs have been suboptimal . authors propose a set-aligning framework for the effective utilisation of Large Language Models . |
| Approach: | They propose a set-aligning framework for the effective utilisation of Large Language Models to alleviate text generation loss penalties. |
| Outcome: | The proposed framework surpasses existing baselines for event temporal graph generation. |
Enhancing Logical Reasoning in Language Models via Symbolically-Guided Monte Carlo Process Supervision (2025.emnlp-main)
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| Challenge: | Large language models have shown strong performance in many reasoning benchmarks, but lack robust planning or symbolic abstractions. |
| Approach: | They propose to synthesize high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale via Monte Carlo estimation. |
| Outcome: | The proposed method can be trained on high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale using Monte Carlo estimation. |