Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations

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We introduce AI rationalization, an approach for generating explanations of autonomous system behavior as if a human had performed the behavior.

• We describe a rationalization technique that uses neural machine translation to translate internal state-action representations of an autonomous agent into natural language.
• We evaluate our technique in the Frogger game environment, training an autonomous game playing agent to rationalize its action choices using natural language.
• A natural language training corpus is collected from human players thinking out loud as they play the game.