| Challenge: | Narrative passages describe a chain of events, which helps the machine understand the passage comprehensively. |
| Approach: | They propose a method to let machine read narrative passages with their prior knowledge . they build a scene graph using Atomic as external knowledge and encode it with GDIN . |
| Outcome: | The proposed method achieves state-of-the-art on a Story Cloze Test and CosmosQA datasets. |
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Narrative Modeling with Memory Chains and Semantic Supervision (P18-2)
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| Challenge: | Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. |
| Approach: | They propose a method that tracks various semantic aspects with external neural memory chains . they propose to encourage each to focus on a particular semantic aspect . |
| Outcome: | The proposed method outperforms baselines on the task of story ending prediction. |
Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) excel in generating coherent texts, but their ability to comprehend the author’s thoughts remains uncertain. |
| Approach: | They conduct a comprehensive survey of narrative understanding tasks, examining their key features, definitions, taxonomy, associated datasets, evaluation metrics, and limitations. |
| Outcome: | The proposed framework could be extended to address novel narrative understanding tasks. |
Reading Comprehension with Graph-based Temporal-Casual Reasoning (C18-1)
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| Challenge: | Existing methods for reading comprehension tasks ignore semantic relations between sentences or use sliding window scanning over the words of the passage without sentence breaks. |
| Approach: | They propose a method to integrate information from multiple sentences to answer complex questions. |
| Outcome: | Experiments on RACE and MCTest show that the proposed approach improves state-of-the-art methods on simple factoid questions. |
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. |
| Approach: | They propose a novel context zoom-in network (ConZNet) that can skip through irrelevant parts of a document and generate an answer using only the relevant regions of text. |
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MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge (L18-1)
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| Challenge: | Various approaches for script knowledge extraction and processing have been proposed in recent years. |
| Approach: | They propose a dataset to evaluate natural language understanding approaches based on commonsense knowledge. |
| Outcome: | The proposed dataset provides test cases for the broader natural language understanding community. |
Narrative Theory for Computational Narrative Understanding (2021.emnlp-main)
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| Challenge: | a growing body of theoretical work on narrative has been focused on the field of natural language processing . this position paper aims to provide a unifying framework for the computational study of narrative . |
| Approach: | They propose to introduce dominant theoretical frameworks to the NLP community and situate current research within distinct narratological traditions. |
| Outcome: | The proposed framework would allow for new empirical questions and applications in the field of natural language processing. |
Narrative Embedding: Re-Contextualization Through Attention (2021.emnlp-main)
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| Challenge: | a novel approach to narrative event representation uses attention to re-contextualize events across the whole story . a recent study shows that attention is used to attach event semantics to tokens . |
| Approach: | They propose an unsupervised approach to narrative event representation using attention to re-contextualize events across the whole story. |
| Outcome: | The proposed approach achieves state of the art performance on multiple choice and story cloze tasks. |
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning (D19-1)
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| Challenge: | Existing reading comprehension datasets focus on factual and literal understanding of context paragraphs, but our dataset focuses on reading between the lines over a diverse collection of everyday narratives. |
| Approach: | They propose a large-scale dataset that requires commonsense-based reading comprehension, formulated as multiple-choice questions. |
| Outcome: | The proposed architecture improves over the baselines of existing reading comprehension datasets and shows a significant gap between machine (68.4%) and human performance (94%). |
Interpreting Themes from Educational Stories (2024.lrec-main)
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| Challenge: | Recent advances in machine reading comprehension (MRC) have centered on literal comprehension, referring to the surface-level understanding of content. |
| Approach: | They propose a dataset specifically designed for interpretive comprehension of educational narratives, providing corresponding well-edited theme texts. |
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On Making Reading Comprehension More Comprehensive (D19-58)
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| Challenge: | Getting machines to "understand" text is a vast and long-standing problem, made more challenging by the fact that it is not even clear what it means to understand text. |
| Approach: | They propose a question-based approach to machine reading comprehension that uses a natural language question to test a system's comprehension of a passage of text. |
| Outcome: | The proposed questions have surface cues or other biases that allow a model to shortcut the intended reasoning process. |