Coherence Modeling of Asynchronous Conversations: A Neural Entity Grid Approach (P18-1)
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| Challenge: | Existing coherence models are not able to distinguish coherent discourses from incoherent ones. |
| Approach: | They propose a novel coherence model for written asynchronous conversations . they propose to lexicalize the model's entity transitions and extend it to asynchron conversations based on conversational structure . |
| Outcome: | The proposed model outperforms existing models on coherence assessment and thread reconstruction tasks. |
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| Challenge: | Existing approaches to model coherence are limited to small newswire corpora . evaluators need to be trained on lexical and document levels to perform evaluations . |
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| Challenge: | Existing models for coherence modeling fail on harder tasks with more realistic application scenarios. |
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| Challenge: | Recent neural coherence models encode the input document using large-scale pretrained language models. |
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| Challenge: | Existing work on coherence modeling focuses on entity features or discourse relation features, with little attention given to combining the two. |
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| Challenge: | Existing models for text coherence assessment rely on a proxy task . however, this approach does not capture the full range of factors contributing to coherency. |
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| Challenge: | Entity grids and entity graphs are two frameworks for modeling local coherence . many approaches to local cohesion modeling rely on entity relations between sentences . |
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| Challenge: | Existing work on coherence modeling has focused on integrating entity-based models. |
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Evaluating Text Coherence at Sentence and Paragraph Levels (2020.lrec-1)
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| Challenge: | Existing text ordering models have been used to test coherence in NLP for a long time. |
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Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments (2020.emnlp-main)
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| Challenge: | Prior studies of coherence focused on identifying semantic relations between adjacent sentences. |
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Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models (2021.naacl-main)
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| Challenge: | a common approach to coherence evaluation is shuffling the sentence order of a text, creating incoherent text samples that need to be discriminated from the original. |
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