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

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