Towards Interpretable Reasoning over Paragraph Effects in Situation (2020)

Abstract: We focus on the task of reasoning over paragraph effects in situation, which requires a model to understand the cause and effect described in a background paragraph, and apply the knowledge to a novel situation. Existing works ignore the complicated reasoning process and solve it with a one-step “black box” model. Inspired by human cognitive processes, in this paper we propose a sequential approach for this task which explicitly models each step of the reasoning process with neural network modules. In particular, five reasoning modules are designed and learned in an end-to-end manner, which leads to a more interpretable model. Experimental results on the ROPES dataset demonstrate the effectiveness and explainability of our proposed approach.

## introduction as a long-standing fundamental task of natural language processing, machine reading comprehension (mrc) has attracted remarkable attention recently and different mrc datasets have been studied @xcite @xcite , among which reasoning over paragraph effects in situation (ropes for short) is a very challenging scenario that needs to understand knowledge from a background paragraph and apply it to answer questions in a novel situation. ## related work neural network modules have been studied by several works. @xcite propose neural module networks with a semantic parser on visual question answering. @xcite apply a self-assembling modular network with only three modules: find, relocate and compare to hotpot qa @xcite . @xcite extend the neural module networks to answer compositional questions against a paragraphs of text as context, and perform symbolic reasoning on the self-pruned subset of drops @xcite . compared with them, we focus on a more challenging mrc task: reasoning over paragraph effects in situation, which has been rarely investigated and needs more complex reasoning. so far as we know, the only two works (i.e. @xcite and @xcite ) on this topic uses a one-step "black box" model. such an approach performs well on some questions at the expense of limited intepretability. our work solves this task in a logical manner and exposes intermediate reasoning steps which improves performance and interpretability concurrently. ## methodology as shown in figure 1 , our approach consists of three components which are contextual encoding, interpretable reasoning, and answer prediction. ## conclusion and future work in this paper, we aim to answer ropes questions in an interpretable way by leveraging five neural network modules. these modules are trained in an end-to-end manner and each module provides transparent intermediate outputs. experimental results demonstrate the effectiveness of each module, and analysis on intermediate outputs presents good interpretability for the inference process in contrasted with "black box" models. moreover, we find that with explicitly designed compositional modeling of inference process, our approach with a few training examples achieves similar accuracy to strong baselines with full-size training data which indicates a better generalization capability. meanwhile, extending these models to a larger scope of question types or more complex scenarios is still a challenge, and we will further investigate the trade-off between explainability and scalability.