Papers by Lihui Liu
HyperKGR: Knowledge Graph Reasoning in Hyperbolic Space with Graph Neural Network Encoding Symbolic Path (2025.emnlp-main)
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| Challenge: | Existing methods for linking knowledge graphs are incomplete and rely on Euclidean embeddings . a hyperbolic GNN framework embeds recursive learning trees in hyperbolical space . |
| Approach: | They propose a hyperbolic GNN framework that embeds recursive learning trees in hyperbolical space and generates query-specific embeddings. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on multiple benchmark datasets. |
Conversational Question Answering with Language Models Generated Reformulations over Knowledge Graph (2024.findings-acl)
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| Challenge: | State-of-the-art ConvQA methods struggle with inexplicit question-answer pairs, which can degrade ConvQ performance. |
| Approach: | They propose a reinforcement learning based model, CoRnNet, which utilizes question reformulations generated by large language models to improve ConvQA performance. |
| Outcome: | The proposed model outperforms state-of-the-art ConvQA models by using question reformulations generated by large language models (LLMs). |
SimCSE++: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives (2023.emnlp-main)
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| Challenge: | Experimental results show that combining both proposed methods leads to a gain of 1.8 points compared to the strong baseline SimCSE configured with BERT base. |
| Approach: | They propose a method to deal with dropout noise and a dimension-wise contrastive learning objective to address feature corruption. |
| Outcome: | The proposed method achieves 1.8 points compared to the strong baseline SimCSE and 1.4 points for DiffCSE. |
DistillCSE: Distilled Contrastive Learning for Sentence Embeddings (2023.findings-emnlp)
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| Challenge: | Existing approaches to sentence embeddings are based on contrastive learning (CL) . |
| Approach: | They propose a framework which performs contrastive learning under the self-training paradigm with knowledge distillation and propose 'Group-P shuffling strategy' and averaging logits from multiple teacher components. |
| Outcome: | The proposed framework outperforms many strong baseline methods and yields a new state-of-the-art performance. |
On Synthetic Data for Back Translation (2022.naacl-main)
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| Challenge: | Existing studies on back translation (BT) focus on beam search or random sampling . a new method to generate synthetic data with a backward model is proposed to improve BT performance. |
| Approach: | They propose a method to generate synthetic data to trade off quality and importance factors . back translation (BT) is one of the most significant technologies in NMT research fields . |
| Outcome: | The proposed method outperforms the baseline methods on WMT14 DE-EN, EN-DE, and RU-EN benchmark tasks. |