Papers with document comprehension
Dialog Generation Using Multi-Turn Reasoning Neural Networks (N18-1)
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| Challenge: | Existing methods for dialog generation are limited and short at generalization. |
| Approach: | They propose a generalizable dialog generation approach that adapts multi-turn reasoning to generate responses by taking current conversation session context as a document and current query as 'question' they separate the single memory used for document comprehension into different groups for speaker-specific topic and opinion embedding. |
| Outcome: | Experiments on Japanese 10-sentence (5-round) conversation modeling show that multi-turn reasoning can produce more diverse and acceptable responses than state-of-the-art single-turn and non-reasoning baselines. |
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge (2022.acl-long)
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| Challenge: | Existing methods to improve machine reading comprehension (MRC) tasks require unstated knowledge to perform well. |
| Approach: | They propose to extract a new kind of structured knowledge from scripts and use it to improve machine reading comprehension (MRC) They propose a teacher-student paradigm to facilitate the transfer of knowledge in weakly-labeled MRC data. |
| Outcome: | The proposed method outperforms methods that use weakly-labeled data and improves a state-of-the-art baseline by 4.3% in accuracy on a Chinese multiple-choice MRC dataset C3. |
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding (2024.lrec-main)
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| Challenge: | Existing methods to enhance document comprehension require fine-tuning for each task and dataset, and are expensive to train and operate. |
| Approach: | They propose a more flexible document analysis method that integrates visual-rich document understanding with large-scale language models (LLMs) by leveraging existing research in document image understanding and LLMs’ superior language understanding capabilities, the proposed model performs an understanding of document images in a single model. |
| Outcome: | The proposed model improves on the baseline model in document image understanding tasks. |
LC-Eval: A Bilingual Multi-Task Evaluation Benchmark for Long-Context Understanding (2025.findings-emnlp)
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Sheikh Jubair, Arwa Omayrah, Amal Alshammari, Alhanoof Althnian, Abdulhamed Alothaimen, Norah A. Alzahrani, Shahad D. Alzaidi, Nora Al-Twairesh, Abdulmohsen Al-Thubaity
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated sophisticated capabilities, including the ability to process and comprehend extended contexts. |
| Approach: | They propose a bilingual, multi-task evaluation benchmark designed to evaluate long-context understanding in English and Arabic. |
| Outcome: | The proposed benchmark targets context lengths ranging from 4k to over 128k tokens. |