Papers by Meng Liao
Parameter-Efficient Fine-Tuning without Introducing New Latency (2023.acl-long)
Copied to clipboard
| Challenge: | Parameter-efficient fine-tuning of pre-trained language models has been demonstrated to be effective, but its inherent characteristics limit its performance. |
| Approach: | They propose to generate a sparse mask in a task-agnostic manner by modifying only a small subset of existing parameters and adding new parameters. |
| Outcome: | The proposed method surpasses existing methods on the GLUE benchmark by a significant margin. |
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction (2022.coling-1)
Copied to clipboard
| Challenge: | Existing solutions for quotation extraction use rule-based approaches and sequence labeling models. |
| Approach: | They propose a Context and Former-Label Enhanced Net for quotation extraction. |
| Outcome: | The proposed method achieves state-of-the-art performance on complicated quotation extraction on two public datasets and one proprietary dataset. |
A Dashboard for Mitigating the COVID-19 Misinfodemic (2021.eacl-demos)
Copied to clipboard
Zhengyuan Zhu, Kevin Meng, Josue Caraballo, Israa Jaradat, Xiao Shi, Zeyu Zhang, Farahnaz Akrami, Haojin Liao, Fatma Arslan, Damian Jimenez, Mohanmmed Samiul Saeef, Paras Pathak, Chengkai Li
| Challenge: | a new public dashboard aims to understand the impact of the COVID-19 misinfodemic on Twitter . the dashboard uses a curated catalog of COVId-19 related facts and debunks of misinformation . |
| Approach: | They propose a public dashboard that matches tweets with COVID-19 misinformation . they also propose experiments to analyze the spread of misinformation on twitter . |
| Outcome: | The proposed dashboard uses a curated catalog of COVID-19 related facts and debunks misinformation . it shows the most prevalent information from the catalog among Twitter users in user-selected geographic regions . |
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies show that pre-trained masked language models can be factual knowledge bases. |
| Approach: | They conduct a rigorous study to explore the underlying predicting mechanisms of MLMs . they find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts a . |
| Outcome: | The proposed model improves on illustrative cases and external contexts . the results question the previous findings that MLMs can be reliable factual knowledge bases . |
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks. |
| Approach: | They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm . |
| Outcome: | The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings. |
Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding (2024.acl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have yielded remarkable performance, but objective mismatch issues hinder RLHF learning. |
| Approach: | They propose a Reinforcement Learning framework enhanced with Label-sensitive reward to enhance LLMs' alignment and generation capabilities. |
| Outcome: | The proposed framework improves performance on five diverse models across eight tasks. |
EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing benchmarks often overlook intra-language variations, leaving speakers of non-standard dialects underserved. |
| Approach: | EnDive evaluates seven state-of-the-art large language models across tasks . human evaluations confirm high translation quality, with average scores of at least 6.02/7 . |
| Outcome: | EnDive evaluates state-of-the-art large language models across language understanding, reasoning, mathematics, logic tasks. |
Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors (2024.acl-long)
Copied to clipboard
| Challenge: | Multiple-Choice Questions (MCQs) are a critical area of research in the study of Large Language models (LLMs). |
| Approach: | They propose an efficient SFT algorithm for MCQs, termed Point-wise Intelligent Feedback, which constructs negative instances by randomly combing the incorrect option contents with all candidate symbols. |
| Outcome: | The proposed algorithm significantly reduces the model’s selection bias by improving its MCSB capability. |
Seg2Act: Global Context-aware Action Generation for Document Logical Structuring (2024.emnlp-main)
Copied to clipboard
Zichao Li, Shaojie He, Meng Liao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Yanxiong Lu, Xianpei Han, Le Sun
| Challenge: | Document logical structuring is crucial for document intelligence due to the complexity of text segment dependencies in the document. |
| Approach: | They propose an end-to-end, generation-based method for document logical structuring that generates the action sequence via a global context-aware generative model and updates its global context and current logical structure based on the generated actions. |
| Outcome: | Experiments on ChCatExt and HierDoc datasets show that Seg2Act performs better than previous methods in both supervised and transfer learning settings. |
From Discourse to Narrative: Knowledge Projection for Event Relation Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing event-centric knowledge graphs rely on explicit connectives to extract relations between events. |
| Approach: | They propose a knowledge projection paradigm for event relation extraction using commonalities between events. |
| Outcome: | The proposed method achieves state-of-the-art performance and extrinsic results verify the extracted event relations. |
WebDP: Understanding Discourse Structures in Semi-Structured Web Documents (2023.findings-acl)
Copied to clipboard
| Challenge: | Web documents are one of the most primary and biggest data resources in current era, and understanding their discourse structure will benefit various downstream document processing applications. |
| Approach: | They propose a web document discourse structure representation schema by extending classical discourse theories and adding special features to well represent discourse characteristics of web documents. |
| Outcome: | The proposed task is feasible but challenging for current models. |
ECO v1: Towards Event-Centric Opinion Mining (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on event-centric opinion mining focus on entity-centric opinions . entity-centered opinions focus on sentimental polarity of events, while event-centered ones focus on content . |
| Approach: | They propose to perform event-centric opinion mining on event-argument structure and expression categorizing theory and benchmark it against a pioneer corpus. |
| Outcome: | The proposed task is feasible and challenging, and the results are beneficial for future studies. |