DECIDER's approach differs from traditional unconstrained decoding methods in several key ways.
Firstly, DECIDER introduces a rule-controllable decoding strategy that focuses on following high-level rules rather than just targeting specific keywords or concepts. This allows the model to generate text in a more human-like manner by considering not only the targets but also semantically relevant concepts that contribute to achieving the targets.
Secondly, DECIDER incorporates a logical reasoner and decision function into the decoding process. The logical reasoner evaluates high-level rules expressed in First-Order Logic (FOL) to determine which words meet the rule criteria. The decision function then combines decisions from both neural and logic systems to guide generation towards words that satisfy the rules.
Overall, DECIDER's approach shifts away from simply optimizing for target satisfaction towards incorporating higher-level reasoning and controllability into language generation tasks.