IRRGN : An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection (2022)

Abstract: The task of response selection in multi-turn dialogue is to find the best option from all candidates. In order to improve the reasoning ability of the model, previous studies pay more attention to using explicit algorithms to model the dependencies between utterances, which are deterministic, limited and inflexible. In addition, few studies consider differences between the options before and after reasoning. In this paper, we propose an Implicit Relational Reasoning Graph Network to address these issues, which consists of the Utterance Relational Reasoner (URR) and the Option Dual Comparator (ODC). URR aims to implicitly extract dependencies between utterances, as well as utterances and options, and make reasoning with relational graph convolutional networks. ODC focuses on perceiving the difference between the options through dual comparison, which can eliminate the interference of the noise options. Experimental results on two multi-turn dialogue reasoning benchmark datasets MuTual and MuTualplus show that our method significantly improves the baseline of four pre-trained language models and achieves state-of-the-art performance. The model surpasses human performance for the first time on the MuTual dataset.

## introduction the response selection task is one of the most important tasks in neural dialogue systems @xcite @xcite @xcite @xcite , which aims to find the most appropriate response from a set of candidate options given a historical dialogue. most previous studies focus on matching between candidate options and historical dialogues while ignoring the reasoning ability of the model. this causes the model to choose logically incorrect or even counter-common-sense options, resulting in a poor user experience @xcite . on the recently released multi-turn dialogue reasoning benchmark dataset mutual @xcite , these traditional representative response selection models @xcite perform poorly, which indicates that comparing with matching, reasoning ability is more important in mutual. specifically, matching finds semantically related candidates, while reasoning requires obtaining logically consistent responses based on logical and semantic dependencies between sentences. for example, in figure 1 , all the relevant words that appear in option c include "honey" and "birthday", both of which occur in historical dialogue. since traditional models tend to choose the more semantically relevant option, they consider option c as the best option. however, option c is not logically consistent with the historical dialogue. this conflicts with option c because the hat has already been found in the history dialogue. similarly, options b and d also have the above problems. for the option a, "married" can be inferred from "each child", "honey" and "the kids", and it would be considered by a model with good reasoning ability. there are some work has emerged to improve the reasoning ability of models, but they still have some shortcomings. firstly, modeling the dependencies between sentences is an important part of improving the reasoning ability of the model. experience shows that temporal dependencies @xcite between sentences as well as semantic dependencies are critical for multi-turn response selection. previous studies typically historical dialogues and candidate options as the context @xcite , or process each utterance independently @xcite , which lead to ignoring dependencies between sentences. there are also methods to model dependencies through explicit rules, such as using some sentiment dictionaries to obtain features or relying on some community detection algorithm for modeling @xcite . however, the dependencies established by these explicit rules are deterministic and limited, which affects the performance of the model and can not establish the flexible dependencies between the utterances. in addition, previous models only focus on the reasoning method, while ignoring the difference between candidate options before and after reasoning. generally, people first compare the candidate options to understand each of them. after that, people read the article or historical dialogue information to make a deep reasoning and finally make a correct comparison between the candidate options again. taking figure 1 as an example, humans first compare the differences among the four options and find that the relationship between speakers differs the most. then based on historical conversation information, words such as "child", "kids" and "honey" are captured. comparing the differences between the four options again, it is found that only option a matches the historical dialogue, and finally, conclude that a is the best option. inspired by human behaviors in reasoning, one can easily come to the correct answer after a two-stage comparison, which is similar to the preview and read methods in the pq4r learning strategy. based on the above ideas, in this paper, we propose implicit relational reasoning graph network (irrgn), which consists of the utterance relation reasoner (urr) and the option dual comparator (odc). specifically, the purpose of the urr is to reason and adaptively capture flexible dependencies between utterances, as well as utterances and options, without relying on any explicit algorithm. the odc is used to perceive the difference between the options before and after reasoning, which can eliminate the interference of the noise options. in summary, our contributions are as follows: ## model the architecture is shown in figure 2 , which is divided into six modules in total. firstly, the context encoder encodes representations of historical dialogues and candidate options. secondly, the option dual comparator (before) compares the representation differences of options before reasoning. then the utterance relation reasoner (urr) grasps the different dependencies between utterances, as well as utterances and options, and makes a reasoning between the historical dialogue and candidate options to improve the reasoning ability of the model. next, the option dual comparator (after) module compares the differences of the options after reasoning. finally, the prediction layer is used to calculate the score of the options. ## experiments in this section, we conduct experiments on the mu-tual dataset and mutual plus dataset to evaluate our proposed irrgn. in all comparative experiments, in order to ensure the authenticity of the experimental results, all training hyperparameters are kept the same. only adjust the learning rate when the model does not converge. ## conclusion in this paper, we propose a novel implicit relational reasoning graph network (irrgn). it can implicitly define dependencies between utterances, as well as utterances and options for more efficient and flexible graph reasoning. among other things, it captures the differences between options before and after reasoning. state-of-the-art performance is achieved on the mutual and mutual plus datasets that focus on the multi-turn dialogue reasoning task. in future work, we will further implement more fine-grained reasoning, explore model interpretability through bad cases, and let the model consider security responses.