Adapting Text Generation to Individual Users
Project A8 is concerned with how to write a text in a way that a given reader can understand optimally. Our starting point is that readers differ in their cognitive properties, and that therefore, different choices of linguistic encoding will be optimal for different readers. We investigate this issue from two perspectives. In our psycholinguistic work packages, we will investigate how the interplay of cognitive properties and linguistic encoding affects comprehension, and how a specific reader’s cognitive properties can be inferred from their eye movements during reading. From a computational perspective, we will develop text-to-text generation systems which diagnose the reader’s cognitive properties and then rewrite a given text to be optimal for that reader.
WP 1 will be concerned with classifying readers with respect to their cognitive properties such as working memory capacity, lexical and linguistic knowledge based on their behaviour during reading. WP 2 aims to relate these cognitive properties to differences in language comprehension of different comprehenders, and identify how language should be adapted to benefit an individual’s comprehension. In WP 3, we will develop methods for manipulating generated text in terms of lexical and syntactic complexity, while WP 4 will focus on strategies for automatically adding or removing information from a text in order to fit a reader’s background knowledge. Finally, in WP 5, the diagnostic results from WP 1 and their implications for comprehension, as determined by WP 2, will be used to automatically adapt text production using methods from WPs 3 and 4, while also taking into account uncertainty about user properties.
Keywords: text generation
Other Area-A Projects
- Neurobehavioural Correlates of Surprisal in Online Comprehension A1
- The Role of Language Experience and Surprisal for Learning and Memory A5
- Expectancy-based mechanisms during language comprehension and their relation to memory formation and retrieval A6
- Controlling Information Density in Discourse Generation A7