Multi-Source Probing for Open-Domain Conversational Understanding (2023)

Abstract: Dialogue comprehension and generation are vital to the success of open-domain dialogue systems. Although pre-trained generative conversation models have made significant progress in generating fluent responses, people have difficulty judging whether they understand and efficiently model the contextual information of the conversation. In this study, we propose a Multi-Source Probing (MSP) method to probe the dialogue comprehension abilities of open-domain dialogue models. MSP aggregates features from multiple sources to accomplish diverse task goals and conducts downstream tasks in a generative manner that is consistent with dialogue model pre-training to leverage model capabilities. We conduct probing experiments on seven tasks that require various dialogue comprehension skills, based on the internal representations encoded by dialogue models. Experimental results show that open-domain dialogue models can encode semantic information in the intermediate hidden states, which facilitates dialogue comprehension tasks. Models of different scales and structures possess different conversational understanding capabilities. Our findings encourage a comprehensive evaluation and design of open-domain dialogue models.

## introduction conversational understanding and response generation are critical for the success of open-domain dialogue systems. recently, pre-trained open-domain dialogue models, including dialogpt @xcite , blenderbot @xcite , and meena @xcite , have achieved impressive progress in a wide range of conversational tasks. the pre-trained model has become a solid foundation for the downstream fine-tuning process, such as generating empathetic @xcite and persona-coherent @xcite responses, delivering knowledge-grounded conversations @xcite , and completing task goals @xcite . while these generative dialogue models can produce fluent responses, they still have many limitations in conversational understanding @xcite , leading to irrelevant, repetitive, and generic responses @xcite @xcite . research on conversational understanding can provide a holistic evaluation of dialogue models @xcite , contributing to the deployment and design of models. however, the analysis of open-domain dialogue models on conversational understanding remains a controversial topic @xcite @xcite . some work @xcite demonstrates that dialogue models have difficulty in capturing the conversational dynamics in the dialog history and struggle with conversational understanding tasks such as question answering, contradiction inference, and topic determination. in contrast, @xcite affirms the conversational understanding of open-domain dialogue models and indicates recurrent dialogue models perform better than transformer-based models. these studies have limitations in probing methods and experimental settings @xcite , making them not applicable to present largescale open-domain dialogue models. in this work, we propose a multi-source probing (msp) method to examine the conversational understanding ability of open-domain dialogue models. specifically, msp conducts dialogue comprehension tasks in a generative manner, which is coherent with the pre-trained dialogue generation task to take full advantage of model capabilities. in addition, considering that different tasks require various information of the dialogue context, msp aggregates features from multiple sources to accomplish diverse tasks. we propose a multi-source crossattention mechanism to extract local features and adopt a late fusion module to incorporate global features. with the help of these components, msp has the capability to evaluate generative dialogue models more accurately and comprehensively. to expose the validity and reliability of our method, we conduct comprehensive experiments to compare the conventional mlp-based probing approach with msp. furthermore, we also set up a series of ablation experiments to verify the necessity of the multiple-source attention mechanism and the late-fusion module. in order to verify that our method also applies to even more massive scale models, we extend our msp method to provide insight into larger pretrained dialogue models. we found that larger models have stronger extraction capability for the information inferred via the pre-trained encoder. our study reveals three critical findings: ## analysis we will detail experimental results in this section, including the analysis of our main experiments on the performance of different probing methods, the ablation study of multi-source probing architectures, and evaluations of different dialogue models and experimental settings. ## conclusion in this paper, we propose a multi-source probing (msp) method to probe the dialogue comprehension abilities of open-domain dialogue models. it conducts probing tasks in a generative manner that is consistent with the pre-training task of dialogue models. besides, we propose the multi-source attention mechanism to aggregate features from multiple sources and the late fusion module to capture global features for downstream tasks. our experimental results indicate the validity and reliability of the msp method, which could also offer insight into the impact of the model scale, embedding quality, and dialogue structure on the conversational understanding capability of dialogue models when particular experimental settings are presented. this research underscores the importance of a comprehensive probing framework for dialogue models and paves the way for future studies aimed at enhancing their understanding capabilities.