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

TL;DR

This paper introduces a new loss function and evaluation metric for dialogue generation that incorporate semantic relevance and context, leading to improved performance over traditional methods.

Contribution

It proposes SemTextualLogue loss and Dialuation metric, addressing limitations of lexical-based metrics by integrating semantics and context into dialogue modeling.

Findings

Models trained with SemTextualLogue outperform traditional loss functions.

Dialuation metric correlates better with human judgment.

Semantic and contextual integration improves dialogue response quality.

Abstract

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. These lexical-based metrics, e.g., cross-entropy and BLEU, have two 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. We also formulate an evaluation metric called Dialuation, incorporating both context and semantic relevance. We experimented with both non-pretrained and pre-trained models on two dialogue corpora, encompassing task-oriented and open-domain scenarios. We found that the dialogue generation models trained with SemTextualLogueloss attained superior performance compared to the traditional cross-entropy loss function. The findings establish that the effective training of a dialogue generation model hinges significantly on incorporating semantics and context. This pattern is also mirrored in the introduced Dialuation metric, where the consideration of both context and semantics correlates more strongly with human evaluation compared to traditional metrics.