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Over the past two decades, dialogue modeling has made significant strides,moving from simple rule-based responses to personalized and persuasive responsegeneration. However, despite these advancements, the objective functions andevaluation metrics for dialogue generation have remained stagnant, i.e.,cross-entropy and BLEU, respectively. These lexical-based metrics have thefollowing key limitations: (a) word-to-word matching without semanticconsideration: It assigns the same credit for failure to generate 'nice' and'rice' for 'good'. (b) missing context attribute for evaluating the generatedresponse: Even if a generated response is relevant to the ongoing dialoguecontext, it may still be penalized for not matching the gold utterance providedin the corpus. In this paper, we first investigate these limitationscomprehensively and propose a new loss function called Semantic InfusedContextualized diaLogue (SemTextualLogue) loss function. Furthermore, weformulate a new evaluation metric called Dialuation, which incorporates bothcontext relevance and semantic appropriateness while evaluating a generatedresponse. We conducted experiments on two benchmark dialogue corpora,encompassing both task-oriented and open-domain scenarios. We found that thedialogue generation model trained with SemTextualLogue loss attained superiorperformance (in both quantitative and qualitative evaluation) compared to thetraditional cross-entropy loss function across the datasets and evaluationmetrics.

Hi Model, generating 'nice' instead of 'good' is not as bad as  generating 'rice'! Towards Context and Semantic Infused Dialogue Generation  Loss Function and Evaluation Metric