How can you evaluate the coherence of a generated text?

3 How can you use coherence metrics?

In order to evaluate the coherence of a generated text, quantitative metrics can be used to capture some aspects of coherence. These metrics typically focus on lexical cohesion (the repetition or similarity of words or phrases), syntactic structure (grammatical patterns and relations), semantic relations (meaning and logic of concepts and propositions), and discourse structure (organization and transitions of segments and elements). Examples of coherence metrics include entity grid (a matrix representing the distribution and roles of entities across sentences), centering theory (a framework modeling the focus and continuity of discourse entities across sentences), topic models (probabilistic models identifying latent topics/themes within a text), and rhetorical structure theory (a theory describing hierarchical and relational structure of discourse segments).

4 How can you use coherence evaluation systems?

Evaluating the coherence of a generated text can be done using automated systems that score or rank the text. These systems are based on supervised learning, unsupervised learning, or reinforcement learning. For instance, the Coherence Scoring System (CSS) is a supervised system that uses a neural network to predict the coherence score of a text based on entity grid features. Neural Text Coherence Assessment (NTC) is another supervised system that uses a neural network to predict the coherence score of a text based on sentence embeddings and attention mechanisms. On the other hand, Coherence Ranking System (CRS) is an unsupervised system that uses a ranking algorithm to compare the coherence of different texts or versions of a text based on topic models and discourse structure features. Lastly, Neural Text Coherence Optimization (NTCO) is a reinforcement system that uses a generative model and a discriminator model to generate or improve the coherence of a text based on a reward function.

5 How can you use human judgments?

The most reliable and valid way to evaluate the coherence of a generated text is through human judgments. However, they can be costly, time-consuming, and subjective, so they should be used with caution and in conjunction with other methods. Factors that can influence human judgments include the criteria and instructions for the evaluation task, the scale and format of the evaluation ratings, the background and expertise of the evaluators, and the reliability and agreement of the evaluators. Examples of human judgment methods include Likert scale (which asks evaluators to rate coherence on a numerical scale), pairwise comparison (which asks evaluators to compare two texts and choose the more coherent one), and holistic assessment (which asks evaluators to provide an overall impression or comment).