In recent years, large pretrained models have been used in dialogue systems to improve successful task completion rates. However, lack of reasoning capabilities of dialogue platforms make it difficult to provide relevant and fluent responses, unless the designers of a conversational experience spend a considerable amount of time implementing these capabilities in external rule based modules. In this work, we propose a novel method to fine-tune pretrained transformer models such as Roberta and T5. to reason over a set of facts in a given dialogue context. Our method includes a synthetic data generation mechanism which helps the model learn logical relations, such as comparison between list of numerical values, inverse relations (and negation), inclusion and exclusion for categorical attributes, and application of a combination of attributes over both numerical and categorical values, and spoken form for numerical values, without need for additional training dataset. We show that the transformer based model can perform logical reasoning to answer questions when the dialogue context contains all the required information, otherwise it is able to extract appropriate constraints to pass to downstream components (e.g. a knowledge base) when partial information is available. We observe that transformer based models such as UnifiedQA-T5 can be fine-tuned to perform logical reasoning (such as numerical and categorical attributes' comparison) over attributes that been seen in training time (e.g., accuracy of 90\%+ for comparison of smaller than $k_{\max}$=5 values over heldout test dataset).

本篇论文提出了一种基于预训练的Transformer模型fine-tune的方法，使之具备在对话语境中进行逻辑推理和回答问题的能力，而无需额外的训练数据集。研究结果表明，该模型在已训练属性上能够达到90%以上的精度，在适当的对话语境下能够提取出适当的限制条件传递给下游组件(e.g. 知识库)。

面向任务的对话系统的逻辑推理