Explainable Multi-hop Verbal Reasoning Through Internal Monologue (2021)

Abstract: Many state-of-the-art (SOTA) language models have achieved high accuracy on several multi-hop reasoning problems. However, these approaches tend to not be interpretable because they do not make the intermediate reasoning steps explicit. Moreover, models trained on simpler tasks tend to fail when directly tested on more complex problems. We propose the Explainable multi-hop Verbal Reasoner (EVR) to solve these limitations by (a) decomposing multi-hop reasoning problems into several simple ones, and (b) using natural language to guide the intermediate reasoning hops. We implement EVR by extending the classic reasoning paradigm General Problem Solver (GPS) with a SOTA generative language model to generate subgoals and perform inference in natural language at each reasoning step. Evaluation of EVR on the RuleTaker synthetic question answering (QA) dataset shows that EVR achieves SOTA performance while being able to generate all reasoning steps in natural language. Furthermore, EVR generalizes better than other strong methods when trained on simpler tasks or less training data (up to 35.7% and 7.7% absolute improvement respectively).

## introduction large pretrained language models such as bert @xcite and roberta @xcite have been successfully used in multi-hop reasoning problems @xcite @xcite . usually, these pretrained language models solve multi-hop reasoning problems in a discriminative end-to-end manner: these models take the question and all the relevant evidence as the input, and produce the final answer to the question. this raises two problems. first, this direction lacks interpretability, i.e., it is hard to (input facts:) alan is blue. alan is rough. alan is young. bob is big. bob is round. charlie is big. charlie is blue. charlie is green. dave is green. dave is rough. (input rules:) big people are rough. if someone is young and round then they are kind. if someone is round and big then they are blue. all rough people are green. q1: bob is green. true/false? [answer: t] q2: bob is kind. true/false? [answer: f] figure 1 : an example taken from @xcite . the context includes two types of statements: facts and rules. multiple facts and rules are usually needed to answer the questions. for example, to prove "bob is green," the model needs to construct the reasoning chain "all rough people are green ← big people are rough ← bob is big". know which individual reasoning steps are taken in each iteration and why. second, the trained models usually suffer from the compositionality generalization problem, meaning that they tend to fail when the number of reasoning steps are much larger in the evaluation set than in the training set @xcite @xcite . @xcite categorized cognitive processes based on their time scales: unconscious activities take around 50 ms, whereas conscious actions can vary from 100 ms to hours. importantly, @xcite argued that conscious actions are sequences of simple conscious/unconscious actions. extrapolating from cognitive science to natural language processing (nlp), in this paper we ask the question: can we design an interpretable multi-hop reasoning system that sequentially applies neural networks trained on simpler tasks? further, motivated by the finding from cognitive science that people might use internal monologues to guide their reasoning, we want to explore whether it is possible to use natural language to guide this sequential process. in this paper, we propose a solution for these important questions. we provide a neural implementation for a classic planning/reasoning paradigm that is designed to mimic the human reasoning process: the general problem solver (gps) @xcite . we augment gps with a sota sequence-tosequence (seq2seq) model and apply this model recursively to achieve high interpretability and better generalization of compositionality for a synthetic qa task @xcite . the contributions of our paper are the following: (1) we extend @xcite )'s dataset with natural language intermediate goals/statements necessary to answer each question. ## task description and baseline models we build our approach from the multi-hop reasoning problem proposed in @xcite , which we summarize first. figure 1 shows an example from this dataset. each reasoning problem consists of the context c, the question q and the answer @xmath0, f alse}. c includes facts f and rules r. to answer a question, multiple statements in c need to be combined. ## neural general problem solver for multi-hop verbal reasoning in this section, we first review a classic planning/reasoning paradigm that is designed to mimic the human reasoning process, the general problem solver (gps) @xcite , then propose our neural implementation of it. then the proposed operator is used by the executor e to update the goal @xmath1, o). the cycle stops when the goal is satisfied, or no new operators are proposed. figure 2 (b) shows a toy example from the block world, where the agent starts from the goal state and searches for a sequence of actions to reach the initial state. ## conclusion in this paper we propose the explainable multihop verbal reasoner (evr) to solve a synthetic question answering problem that requires multihop reasoning @xcite . evr answers a question by reducing a complex one to several simple ones, and guiding all reasoning steps with natural language for better interpretability. evaluation of evr shows it achieves high accuracy, suffer less from the compositionality generalization problem, and generalizes well when training data are not abundant.