Enhancing Reasoning Capabilities by Instruction Learning and Chain-of-Thoughts for Implicit Discourse Relation Recognition (2023)

Abstract: The aim of implicit discourse relation recognition is to comprehend the sense of connection between two arguments. In this work, we present a classification method that is solely based on generative models. Our proposed approach employs a combination of instruction templates and in-context learning to refine the generative model for effectively addressing the implicit discourse relation recognition task. Furthermore, we utilize Chain-of-Thoughts to partition the inference process into a sequence of three successive stages. This strategy enables us to fully utilize the autoregressive generative model’s potential for knowledge acquisition and inference, ultimately leading to enhanced performance on this natural language understanding task. The results of our experiments, evaluated on benchmark datasets PDTB 2.0, PDTB 3.0, and the CoNLL16 shared task, demonstrate superior performance compared to previous state-of-the-art models.

## introduction discourse relation recognition refers to identifying the sense of the relation between two arguments. this task is categorized into two types: explicit discourse relation recognition (edrr) and implicit discourse relation recognition (idrr) depending on whether explicit connectives, such as "because" and "but", are present or absent between the argument pair. our work investigates the potential of generative models and natural language generation for improving the performance of idrr. recognizing implicit discourse relations involves comprehending and examining the semantic connections between argument pairs. previous works have commonly employed semantic encoding to enhance the model's classification accuracy @xcite @xcite . generative models have also been utilized for idrr. as an example, generation tasks have been used as an auxiliary task @xcite and in a limited form that restricts prompt learning @xcite . with the emergence of large language models, there is a growing interest in utilizing generative models rather than encoder-only models for nlp applications. however, some studies also suggest that generic generative models do not perform as well as fine-tuning relatively small encoder-only models for nlu tasks @xcite . our experiments also reveal that employing generative models to directly generate relation sense in the context of idrr is an ineffective approach. this work investigates how simple yet effective methods (iicot) can unleash the inference capabilities of generative models. figure 1 illustrates our approach of utilizing a thinking process to guide the model's output (cot). specifically, we do not allow the model to output the relation sense directly; we compel the model to first identify whether the argument pair pertains to implicit or explicit relation data. this approach reduces the unwanted noise generated by explicit data. next, the model ## conclusion we aim to enhance the reasoning capabilities of generative models in idrr by employing a generation task framework and incorporating instruction learning, in-context learning, and cot. through our approach, we achieve a notable improvement over the baseline model, leading to state-of-the-art performance on three benchmark datasets. in our future research, we plan to further investigate the utilization of generative models and even large language models. specifically, we aim to explore the efficacy of larger models, including the implementation of knowledge distillation to transfer knowledge from large models to smaller ones.