| Challenge: | Existing approaches to answer questions based on the full text of books are limited by their unique characteristics. |
| Approach: | They propose a system for answering questions based on the full text of books . they use a memory network to reason and predict an answer, and a novel question generator to improve generalization. |
| Outcome: | The proposed system improves on the recently published NarrativeQA corpus on Who questions . it shows that the proposed system is highly challenging and needs more research . |
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| Challenge: | Automated question answering (QA) from text remains a challenge for humans . a striking gap exists between machine and human performance on NLP tasks . |
| Approach: | They propose a heuristic extractive version of a data set to solve the problem of answer extraction rather than generation. |
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Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study (2021.tacl-1)
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| Challenge: | Recent advances in open-domain question answering (ODQA) have led to human-level performance on many datasets. |
| Approach: | They provide a comprehensive and quantitative analysis about the difficulty of book QA . they compare the results of their research with extensive ODQA experiments . |
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Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering (2023.eacl-main)
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| Challenge: | Existing methods for open-domain question-answering use an open book approach . a recent alternative is to retrieve from a collection of previously-generated question-annwer pairs . |
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Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA? (2021.acl-long)
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| Challenge: | Existing work is limited in using small benchmarks with high test-train overlaps. |
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Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds (P18-1)
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| Challenge: | Question Answering (QA) has primarily focused on knowledge bases or free text as a source of knowledge. |
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LiteraryQA: Towards Effective Evaluation of Long-document Narrative QA (2025.emnlp-main)
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| Challenge: | Existing Question Answering systems are limited by noisy documents and flawed QA pairs. |
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NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets (2020.emnlp-demos)
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| Challenge: | Existing tools for Question Answering (QA) have challenges that limit their use in practice. |
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Adaptive Document Retrieval for Deep Question Answering (D18-1)
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| Challenge: | Existing methods for deep question answering do not understand the exact interplay between document retrieval and machine comprehension. |
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Careful Selection of Knowledge to Solve Open Book Question Answering (P19-1)
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| Challenge: | Open book question answering requires deeper reasoning involving linguistic understanding and common knowledge. |
| Approach: | They propose a dataset that mimics open book question answering to achieve 72.0% accuracy. |
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NoiseQA: Challenge Set Evaluation for User-Centric Question Answering (2021.eacl-main)
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| Challenge: | Question-Answering (QA) systems are deployed in the real world . a lack of research attention has been devoted to studying the issues that arise when people use QA systems. |
| Approach: | They show that component components that precede an answering engine can introduce varied and considerable sources of error. |
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