Papers by Qi Shao
Aggregating Bidirectional Encoder Representations Using MatchLSTM for Sequence Matching (D19-1)
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| Challenge: | Recent work on text sequence matching tasks uses task specific supervised datasets, which are always limited to the amount due to the cost of annotation. |
| Approach: | They propose an aggregation method to combine Bidirectional Encoder Representations from Transformer (BERT) with a MatchLSTM layer for Sequence Matching. |
| Outcome: | The proposed model improves on two publicly available datasets, WikiQA and SNLI. |
Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing (2020.coling-main)
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| Challenge: | Existing methods for multilingual semantic parsing only handle monolingual parsers, while in real world applications such as Chatbot and search engine, we generally need to handle multi-lingual semanticparsing. |
| Approach: | They propose a multi-level alignment pretraining method in a unified architecture for multi-lingual semantic parsing. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a publicly avail-able multi-lingual semantic parsing dataset and a newly constructed dataset. |
Intelligent Document Parsing: Towards End-to-end Document Parsing via Decoupled Content Parsing and Layout Grounding (2025.findings-emnlp)
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| Challenge: | Existing methods fragment document parsing into pipeline of separated subtasks, resulting in incomplete semantics and error propagation. |
| Approach: | They propose an end-to-end document parsing framework that leverages vision-language priors of MLLMs. |
| Outcome: | The proposed method surpasses existing methods significantly in document parsing . it leverages the vision-language priors of MLLMs to decouple parse and layout grounding based on visual information. |
Unsupervised Chinese Word Segmentation with BERT Oriented Probing and Transformation (2022.findings-acl)
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| Challenge: | Existing methods for unsupervised Chinese word segmentation exploit shallow semantic information, which can miss important context. |
| Approach: | They propose to take advantage of deep contextual semantic information with a self-training manner to transform it into explicit word segmentation ability. |
| Outcome: | The proposed approach achieves state-of-the-art F1 score on two CWS benchmark datasets. |
GEM: Gestalt Enhanced Markup Language Model for Web Understanding via Render Tree (2023.emnlp-main)
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| Challenge: | Existing models that use plain HTMLs do not include crucial visual information in the rendered web. |
| Approach: | They propose a Gestalt Enhanced Markup Language Model for hosting visual information without visual input. |
| Outcome: | The proposed model can handle multiple downstream tasks without visual input. |
Is Cognition Consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding (2025.emnlp-main)
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| Challenge: | Multimodal large language models (MLLMs) have shown impressive capabilities in document understanding due to different types of annotation noise in training. |
| Approach: | They propose a method to reduce C&P knowledge conflicts across all tested MLLMs . they propose to use annotation noise to train models to understand document content . |
| Outcome: | The proposed method reduces C&P knowledge conflicts across all tested MLLMs and enhances their performance in both cognitive and perceptual tasks. |
DocHieNet: A Large and Diverse Dataset for Document Hierarchy Parsing (2024.emnlp-main)
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| Challenge: | Existing methods for document hierarchy parsing are limited due to the small scale and inconsistency of datasets. |
| Approach: | They propose a document hierarchy parsing dataset to compensate for the data scarcity problem and propose 'dHP' framework to grasp fine-grained text content and coarse-grounded pattern at layout element level. |
| Outcome: | The proposed framework grasps both fine-grained text content and coarse-grounded pattern at layout element level, enhancing the capacity of pre-trained text-layout models in handling multi-page and multi-level challenges. |
High-Quality Medical Dialogue Synthesis for Improving EMR Generation (2025.emnlp-industry)
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| Challenge: | Existing methods for generating EMRs from doctor-patient dialogues produce rigid and repetitive dialogues. |
| Approach: | They propose a framework that integrates Intent Graph Planning, Dual-Agent Simulation and Rule-Reward Quality Control to generate realistic doctor-patient dialogues. |
| Outcome: | The proposed framework significantly enhances realism, diversity and downstream EMR quality, reducing physician editing efforts. |