Emotional Neural Language Generation Grounded in Situational Contexts
This work addresses the need for more human-like emotional language generation in conversational AI, though it appears incremental as it builds on existing datasets and metrics.
The paper tackled the problem of conversational agents not effectively accounting for emotional content in language generation by developing a language modeling approach that generates affective content based on situational contexts, resulting in a 5-point improvement in perplexity and higher BLEU scores compared to state-of-the-art methods.
Emotional language generation is one of the keys to human-like artificial intelligence. Humans use different type of emotions depending on the situation of the conversation. Emotions also play an important role in mediating the engagement level with conversational partners. However, current conversational agents do not effectively account for emotional content in the language generation process. To address this problem, we develop a language modeling approach that generates affective content when the dialogue is situated in a given context. We use the recently released Empathetic-Dialogues corpus to build our models. Through detailed experiments, we find that our approach outperforms the state-of-the-art method on the perplexity metric by about 5 points and achieves a higher BLEU metric score.