Papers by Qiaoqiao She
Multimodal Table Understanding (2024.acl-long)
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| Challenge: | Existing approaches to understanding tables rely on textual inputs and table images are difficult to access in real-world scenarios. |
| Approach: | They propose a multimodal table understanding problem where the model needs to generate correct responses to various table-related requests based on the given table image. |
| Outcome: | The proposed model outperforms open-source MLLMs on 23 benchmarks under held-in and held-out settings. |
DuReadervis: A Chinese Dataset for Open-domain Document Visual Question Answering (2022.findings-acl)
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| Challenge: | Open-domain question answering is a task that requires answering questions based on a collection of document images. |
| Approach: | They propose to use document images to answer questions using layouts and visual features instead of text. |
| Outcome: | The proposed approach reduces human cost and improves scalability of QA systems by incorporating layouts and visual features. |
QDMR-based Planning-and-Solving Prompting for Complex Reasoning Tasks (2024.lrec-main)
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| Challenge: | Existing Plan-and-Solve prompting methods are difficult to implement for complex questions. |
| Approach: | They propose a plan-and-solve prompting method based on Question Decomposition Meaning Representation (QDMR) it allows LLM to generate a QDMR graph to represent problem-solving logic . |
| Outcome: | The proposed method can represent and execute the problem-solving logic of complex questions more accurately than existing methods. |
Chain-of-Thought Reasoning in Tabular Language Models (2023.findings-emnlp)
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| Challenge: | Existing approaches to extend chain-of-thought reasoning into large language models are not viable in the scenario of privatization deployment or limited resources. |
| Approach: | They propose a framework that extends chain-of-thought reasoning into tabular language models . framework coordinates two TaLMs responsible for CoT generation and answer inference . |
| Outcome: | The proposed framework outperforms the state-of-the-art ChatGPT on the TABMWP dataset by 9.55% (82.60%92.15% in accuracy) with less parameters (0.8B). |
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (P19-1)
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| Challenge: | Recent results show pre-trained language models (LMs) can improve machine reading comprehension (MRC) Experimental results indicate that KT-NET offers significant and consistent improvements over BERT . |
| Approach: | They propose a method that leverages external knowledge bases to improve machine reading comprehension (MRC) KT-NET employs an attention mechanism to select desired knowledge from KBs and fuses selected knowledge with BERT to enable context- and knowledge-aware predictions. |
| Outcome: | The proposed model outperforms baseline models on ReCoRD and SQuAD1.1 benchmarks and ranks 1st on the ReCoDR and SQUAD1.1 leaderboards. |