Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension (2022)

Abstract: Machine reading comprehension (MRC) poses new challenges to logical reasoning, which aims to understand the implicit logical relations entailed in the given contexts and perform inference over them. Due to the complexity of logic, logical connections exist at different granularity levels. However, most existing methods of logical reasoning individually focus on either entity-aware or discourse-based information but ignore the hierarchical relations that may even have mutual effects. This paper proposes a holistic graph network (HGN) that deals with context at both discourse-level and word-level as the basis for logical reasoning to provide a more fine-grained relation extraction. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism to improve the interpretation of MRC systems. Experimental results on logical reasoning QA datasets (ReClor and LogiQA) and natural language inference datasets (SNLI and ANLI) show the effectiveness and generalization of our method, and in-depth analysis verifies its capability to understand complex logical relations.

## introduction machine reading comprehension (mrc) is a challenging task that requires machines to answer a question according to given passages @xcite @xcite . a variety of datasets have been introduced to push the development of mrc to a more complex and more comprehensive pattern, such as conversational mrc @xcite , multi-hop mrc @xcite , and commonsense reasoning @xcite @xcite . in particular, figure 1 : an example from reclor dataset. the example mainly talks about "effective teachers, non-eccentric, eccentric, good communicator". some recent multi-choice mrc datasets pose even greater challenges to the logical reasoning ability of models @xcite which are not easy for humans to do well, either. firstly, all the supporting details needed for reasoning are provided by the context, which means there is no additional commonsense or available domain knowledge. secondly, it is a task of answer selection rather than answer retrieval, which means the best answer is chosen according to their logical fit with the given context and the question, rather than retrieved directly from the context according to the similarity between answers and context. most importantly, the relations entailed in the contexts are much more complex than that of previous mrc datasets owing to the complexity of logic, which is hard to define and formulate. without a targeted design for those challenges, existing pre-trained models, e.g., bert, roberta, fail to perform well in such kind of logical reading comprehension systems @xcite . logical reasoning mrc tasks are usually to find an appropriate answer, given a set of context and question. figure 1 shows an example from reclor dataset @xcite which requires logical reasoning ability to make the correct predictions. as humans, to solve such problems, we usually go through the following steps. firstly, we divide the context into several fragments and figure out the logical relations between each clause, such as transition, continuity, contrast, etc. secondly, we extract the important elements in the context, namely, the objects and topics described by the context, and construct the logical graph with these significant elements. finally, we need to compare the answer statement to the mentioned part in the context and assess its logical fit with the given context. most existing methods of logical reasoning mrc focus on either entity-aware or discoursebased information but ignore the hierarchical relations that may have mutual effects @xcite @xcite @xcite . motivated by the observation above, we model logical reasoning chains based on a newly proposed holistic graph network (hgn) that incorporates the information of element discourse units (edu) @xcite and key phrases (kph) extracted from context and answer, with effective edge connection rules to learn both hierarchical features and interactions between different granularity levels. our contributions are summarized as follows. ( 1) we design an extraction algorithm to extract edu and kph elements as the critical basic for logical reasoning. (2) we propose a novel holistic graph network (hgn) to deal with context at both discourse and word level with hierarchical interaction mechanism that yields logic-aware representation for reasoning. (3) experimental results show our model's strong performance improvements over baselines, across multiple datasets on logical reasoning qa and nli tasks. the analysis demon-strates that our model has a good generalization and transferability, and achieves higher accuracy with less training data. ## methodology logical reasoning mrc tasks aim to find the best answer among several given options based on a piece of context that entails logical relations. formally, given a natural language context c, a question q, and four potential answers @xmath0, a 2 , a 3 , a 4 }. we concatenate them as {c, q, a i } pairs. to incorporate the principle of human inference into our method, we propose a holistic graph network (hgn) as shown in figure 2. our model works as follows. first, we use edu and kph extraction algorithm to get necessary kph nodes ({p j }) and edu nodes ({e j }) from the given pairs. they contain information with different granularity levels and complement each other. based on the extracted kph-edu interaction information and pre-defined rules, we construct the holistic graph. the process of constructing the holistic graph is shown in figure 3 . then we measure the interaction between {e j } and {p j } to obtain logic-aware representations for reasoning. ## conclusion this paper presents a novel method to guide the mrc model to better perform logical reasoning tasks. we propose a holistic graph-based system to model hierarchical logical reasoning chains. to our best knowledge, we are the first to deal with context at both discourse level and phrase level as the basis for logical reasoning. to decouple the interaction between the node features and type features, we apply hierarchical interaction mechanism to yield the appropriate representation for reading comprehension. on the logical qa benchmarks (reclor,