| Challenge: | Recent neural coherence models encode the input document using large-scale pretrained language models. |
| Approach: | They propose an entity-based neural local coherence model which is linguistically more sound than previous models. |
| Outcome: | The proposed model outperforms existing models on three downstream tasks. |
Similar Papers
How coherent are neural models of coherence? (2020.coling-main)
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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 . |
| Approach: | They propose four generic evaluation tasks that capture coherence-specific properties . they aim at capturing correct use of discourse connectives and lexical cohesion . |
| Outcome: | The proposed tasks capture coherence-specific properties, including correct use of discourse connectives, lexical cohesion, temporal consistency among events and participants in a story. |
A Neural Graph-based Local Coherence Model (2021.findings-emnlp)
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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 . |
| Approach: | They propose to use Relational Graph Convolutional Networks to encode entity graphs for measuring local coherence. |
| Outcome: | The proposed model outperforms the neural grid-based model on two coherence evaluation tasks while using 50% fewer parameters. |
A Unified Neural Coherence Model (D19-1)
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| Challenge: | Existing models for coherence modeling fail on harder tasks with more realistic application scenarios. |
| Approach: | They propose a unified coherence model that incorporates sentence grammar, inter-sentence coherent relations, and global coherency patterns into a common neural framework. |
| Outcome: | The proposed model outperforms existing models on local and global discrimination tasks and outperformed existing models by a good margin. |
Incremental Neural Lexical Coherence Modeling (2020.coling-main)
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| Challenge: | Recent work on pretrained language models has led to significant improvements in a range of NLP tasks. |
| Approach: | They propose a coherence model which interprets sentences incrementally to capture lexical relations between them. |
| Outcome: | The proposed model interprets sentences incrementally to capture lexical relations between them. |
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. |
A Cross-Domain Transferable Neural Coherence Model (P19-1)
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Peng Xu, Hamidreza Saghir, Jin Sung Kang, Teng Long, Avishek Joey Bose, Yanshuai Cao, Jackie Chi Kit Cheung
| Challenge: | Existing coherence models do not generalize to unseen categories of text . previous work advocates for generative models for cross-domain generalization . |
| Approach: | They propose a local discriminative neural model with a smaller negative sampling space that can discriminate against incorrect orderings. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a standard benchmark dataset on the Wall Street Journal corpus and multiple challenging settings on Wikipedia articles. |
Coherent or Not? Stressing a Neural Language Model for Discourse Coherence in Multiple Languages (2023.findings-acl)
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| Challenge: | Existing work on coherence assessment using NLMs focuses on properties acquired from stand-alone sentences, but their ability to model discourse and pragmatic phenomena is still unclear. |
| Approach: | They propose to use a Neural Language Model to assess coherence in multiple languages to compare models' performance and to examine their performance in a cross-language scenario. |
| Outcome: | The proposed model can model coherent and incoherent text in multiple languages and in-domain settings. |
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. |
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence (2021.acl-short)
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| Challenge: | Recent neural topic models extract words from documents, but they are not coherent . coherence is crucial for topic models, but many use bag-of-words document representations as input . pre-trained language models are becoming ubiquitous in natural language processing . |
| Approach: | They combine contextualized representations with neural topic models to produce more coherent topics . they say that future improvements in language models will translate into better topic models . |
| Outcome: | The proposed approach produces more meaningful and coherent topics than bag-of-words models and recent neural models. |
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. |
| Approach: | They propose a coherence model which takes discourse structural information into account without relying on human annotations. |
| Outcome: | The proposed model performs state-of-the-art on automated essay scoring and assessing writing quality tasks. |