Interactive Machine Comprehension with Information Seeking Agents (2020.acl-main)
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| Challenge: | Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). |
| Approach: | They propose a method that reframes existing machine reading comprehension (MRC) datasets as interactive, partially observable environments. |
| Outcome: | The proposed method "occludes" the majority of a document’s text and adds context-sensitive commands that reveal "glimpses" of the hidden text to a model. |
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| Challenge: | despite considerable progress, most machine reading comprehension tasks lack sufficient training data to fully exploit powerful deep neural network models. |
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| Challenge: | Multiple-choice question answering (MCQA) requires a model to understand natural languages and understand textual representations. |
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Coreference Reasoning in Machine Reading Comprehension (2021.acl-long)
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| Challenge: | Existing datasets for machine reading comprehension do not reflect the natural distribution and, consequently, the challenges of coreference reasoning. |
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| Challenge: | Recent studies have shown that reading strategies improve comprehension levels for readers lacking adequate prior knowledge. |
| Approach: | They propose three general strategies to improve machine reading comprehension (MRC) by fine-tuning a pre-trained model with strategies and a target task. |
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| Challenge: | Conversational machine comprehension (CMC) is a research track in conversational AI. |
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| Challenge: | Existing models of machine reading comprehension (MRC) are based on cloze style questions or crowdworkers given a short passage from well-edited sources. |
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Machine Reading Comprehension using Case-based Reasoning (2023.findings-emnlp)
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Dung Thai, Dhruv Agarwal, Mudit Chaudhary, Wenlong Zhao, Rajarshi Das, Jay-Yoon Lee, Hannaneh Hajishirzi, Manzil Zaheer, Andrew McCallum
| Challenge: | Current state-of-the-art machine readers do not support case-based reasoning . |
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Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing approaches to machine reading comprehension (MRC) on long texts typically chunk text into equally-spaced segments without considering information from other segments. |
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Cut to the Chase: A Context Zoom-in Network for Reading Comprehension (D18-1)
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| Challenge: | Recent deep-learning based models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span. |
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Tracing Origins: Coreference-aware Machine Reading Comprehension (2022.acl-long)
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| Challenge: | a recent study has enriched pre-trained language models with syntactic, semantic and other linguistic information to improve their performance. |
| Approach: | They use a pre-trained language model to leverage coreference information to enhance word embeddings . they use additional encoder layers to focus on coreference mentions or a relational graph convolutional network to model the coreference relations. |
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