Papers by Zhiwen Xie
EmoS: A High-Fidelity Multimodal Benchmark for Fine-grained Streaming Emotional Understanding (2026.acl-long)
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| Challenge: | Existing benchmarks fail to achieve ecological validity, signal clarity, and reliable fine-grained labeling in multimodal Emotion Recognition (MER) Existing datasets lack spontaneity of real-life interactions, resulting in poor quality and inconsistent data quality. |
| Approach: | They propose a bilingual benchmark to resolve limitations of ecological validity and noise in existing datasets by combining strictly filtered static slices with a dynamic Streaming Monologue subset. |
| Outcome: | EmoS provides trusted ground truth that captures continuous emotional evolution. |
MAKI: Multi-layer Aligned Knowledge Injection for Structure-aware Knowledge Graph Completion with Large Language Models (2026.findings-acl)
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| Challenge: | Existing knowledge graph completion methods struggle to capture structural information in knowledge graphs (KGs) Existing approaches for KGC focus on learning representations of entities and relations through observed structural patterns. |
| Approach: | They propose a multi-layer Aligned Knowledge Injection model that tightly integrates structured KG information into LLMs through multi-layered alignment. |
| Outcome: | The proposed method outperforms state-of-the-art methods on benchmark datasets. |
ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding (2020.acl-main)
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| Challenge: | Existing knowledge graph embedding methods are limited in their expressiveness and lack structural information in the embeddable space. |
| Approach: | They propose to use a relation-aware network to learn query embedding . they first explore the Inception network to further increase interactions between head and relation embedders . |
| Outcome: | The proposed network improves performance on WN18RR and FB15k-237 datasets. |
Learning Query Adaptive Anchor Representation for Inductive Relation Prediction (2023.findings-acl)
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| Challenge: | Existing methods to infer the missing links between entities are limited to the transductive setting . Query Adaptive Anchor Representation (QAAR) model is based on entity-independent features . |
| Approach: | They propose a query adaptive anchor representation model which extracts one opening subgraph and performs reasoning by one time for all candidate triples. |
| Outcome: | The proposed model outperforms state-of-the-art models in relation prediction task. |
A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment (2020.coling-main)
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| Challenge: | Existing methods for cross-lingual entity alignment ignore useful pre-aligned links between two KGs. |
| Approach: | They propose a novel method that jointly learns embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. |
| Outcome: | The proposed method achieves remarkable performance gains on three benchmark cross-lingual entity alignment datasets. |