Papers by Liwei Song
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing pre-trained models for knowledgegraph-to-text generation ignore graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments. |
| Approach: | They propose a graph-text joint representation learning model called JointGT which incorporates a structure-aware semantic aggregation module into each Transformer layer to preserve the graph structure. |
| Outcome: | The proposed model achieves state-of-the-art performance on various KG-to-text datasets. |
RAST: Domain-Robust Dialogue Rewriting as Sequence Tagging (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for dialogue rewriting suffer from the robustness issue, i.e., performances drop dramatically when testing on a different dataset. |
| Approach: | They propose a sequence-tagging-based approach that reduces the search space while preserving the core of the task. |
| Outcome: | The proposed model significantly reduces the search space while still covering the core of the task. |
Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment (2025.emnlp-main)
Copied to clipboard
Jingcheng Deng, Zhongtao Jiang, Liang Pang, Zihao Wei, Liwei Chen, Kun Xu, Yang Song, Huawei Shen, Xueqi Cheng
| Challenge: | Experimental results demonstrate that our method significantly outperforms traditional contrastive learning approaches when using the same amount of data. |
| Approach: | They propose a new contrastive learning method built on embedding conditional probability distributions that integrates two tasks: information compression and conditional distribution alignment. |
| Outcome: | The proposed method outperforms traditional contrastive learning approaches and achieves comparable performance to state-of-the-art models when using the same amount of data. |
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual knowledge graph (KG) alignment rely on entity embeddings derived from monolingual KG structural information. |
| Approach: | They propose a topic entity graph to represent entities with contextual information in KGs. |
| Outcome: | The proposed model outperforms state-of-the-art methods by a large margin. |
Entity Linking within a Social Media Platform: A Case Study on Yelp (D18-1)
Copied to clipboard
| Challenge: | Existing studies on entity linking focus on linking entities to knowledge bases, but on social media platforms, such as Yelp, it can be more practical. |
| Approach: | They propose to link entities within a social media platform with a new entity linking problem. |
| Outcome: | The proposed model can link business mentions to corresponding businesses on a social media platform. |
Universal Adversarial Attacks with Natural Triggers for Text Classification (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequences of words added to text processed by classifier. |
| Approach: | They propose a gradient-based search that aims to maximize the downstream classifier’s prediction loss by using an adversarially regularized autoencoder to generate triggers and propose heuristics to spot such attacks. |
| Outcome: | The proposed algorithms reduce model accuracy while being less identifiable than prior models as per automatic detection metrics and human-subject studies. |