coherence modeling
**Coherence modeling** uses **AI to ensure text flows logically** — assessing and generating text where ideas connect naturally, topics develop smoothly, and readers can follow the narrative or argument without confusion.
**What Is Coherence Modeling?**
- **Definition**: AI assessment and generation of logically flowing text.
- **Goal**: Text where ideas connect naturally and make sense together.
- **Opposite**: Incoherent text with random topic jumps, unclear connections.
**Coherence Aspects**
**Local Coherence**: Adjacent sentences connect logically.
**Global Coherence**: Overall text structure makes sense.
**Topic Continuity**: Topics introduced, developed, concluded smoothly.
**Causal Coherence**: Cause-effect relationships clear.
**Temporal Coherence**: Time sequence logical and clear.
**Referential Coherence**: Pronouns and references unambiguous.
**Why Coherence Matters?**
- **Readability**: Coherent text easier to understand.
- **Text Generation**: AI-generated text must flow naturally.
- **Summarization**: Summaries must be coherent, not just extract sentences.
- **Translation**: Preserve coherence across languages.
- **Essay Grading**: Coherence is key quality indicator.
**AI Approaches**
**Entity Grid Models**: Track entity mentions across sentences.
**Graph-Based**: Model text as graph of connected concepts.
**Neural Models**: RNNs, transformers learn coherence patterns.
**Discourse Relations**: Explicit modeling of sentence relationships.
**Applications**: Text generation quality control, essay grading, summarization, machine translation evaluation, writing assistance.
**Evaluation**: Human judgments, entity-based metrics, neural coherence scoring.
**Tools**: Research systems, coherence evaluation metrics, neural language models with coherence awareness.