Papers with local coherence model

3 papers
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input (N18-1)

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Challenge: Existing approaches to Automated Essay Scoring (AES) are not well-suited to capture adversarially crafted input of grammatical but incoherent sequences of sentences.
Approach: They propose a neural model of local coherence that can effectively learn connectedness features between sentences.
Outcome: The proposed approach strengthens the validity of neural essay scoring models.
A Neural Local Coherence Analysis Model for Clarity Text Scoring (2020.coling-main)

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Challenge: Existing methods for scoring text clarity use local coherence between adjacent sentences . local cohesion is one of the main properties to identify whether a text is well-structured or not.
Approach: They propose a method for scoring text clarity by utilizing local coherence between adjacent sentences.
Outcome: The proposed method improves on the PeerRead benchmark dataset.
A Neural Local Coherence Model for Text Quality Assessment (D18-1)

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Challenge: Existing approaches to local coherence modeling capture text relatedness at the level of sentence-to-sentence transitions.
Approach: They propose a local coherence model that captures the flow of what connects adjacent sentences . they represent the semantics of a sentence by a vector and capture its state at each word .
Outcome: The proposed model is beneficial for readability assessment and essay scoring tasks.

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