Papers by Likang Xiao

2 papers
Tucker Decomposition with Frequency Attention for Temporal Knowledge Graph Completion (2023.findings-acl)

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Challenge: Existing models for temporal knowledge graph completion only consider the combination of one relation with one timestamp, ignoring the global nature of the embedding.
Approach: They propose a temporal knowledge Graph Completion model that captures global temporal dependencies between one relation and the entire timestamp.
Outcome: The proposed model outperforms the state-of-the-art models on three standard TKGC datasets on several metrics.
Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)

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Challenge: Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model.
Approach: They propose a model merging framework that modulates the contribution of each source model.
Outcome: Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages.

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