FrESH - Feedback-reliant Enhancement of Subjective Content Descriptions by Humans

An agent in pursuit of a task may work with a corpus containing text documents. To perform information retrieval on the corpus, the agent internally maintains a model of the documents in the corpus. This model may contain annotations such as Subjective Content Descriptions (SCD)—additional data associated with different sentences of documents. In our scenario, a human interacts with the information retrieval agent: The human sends a query to the agent, the agent uses its internal model to calculate a response and returns this response. However, the response may contain erroneous parts. Such errors, like faulty SCDs, may be send back to the agent by the human as feedback. Then, the agent can incorporate the feedback to improve its internal model. However, removing a faulty association of a sentence with an SCD in a previously trained model is a difficulty task—often the model needs to be retrained from scratch. To circumvent this, this paper presents FrESH an approach for Feedback-reliant Enhancement of Subjective Content Descriptions by Humans. Using FrESH the model keeps fresh and maintained with human feedback.