T ransfer N et: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph (2021)

Abstract: Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer. The relations can be represented in terms of labels in knowledge graph (e.g., spouse) or text in text corpus (e.g., they have been married for 26 years). Existing models usually infer the answer by predicting the sequential relation path or aggregating the hidden graph features. The former is hard to optimize, and the latter lacks interpretability. In this paper, we propose TransferNet, an effective and transparent model for multi-hop QA, which supports both label and text relations in a unified framework. TransferNet jumps across entities at multiple steps. At each step, it attends to different parts of the question, computes activated scores for relations, and then transfer the previous entity scores along activated relations in a differentiable way. We carry out extensive experiments on three datasets and demonstrate that TransferNet surpasses the state-of-the-art models by a large margin. In particular, on MetaQA, it achieves 100% accuracy in 2-hop and 3-hop questions. By qualitative analysis, we show that TransferNet has transparent and interpretable intermediate results.

## introduction question answering (qa) plays a central role in artificial intelligence. it requires machines to understand the free-form questions and infer the answers by analyzing information from a large corpus @xcite @xcite or structured knowledge base @xcite @xcite . along with the fast development of deep learning, especially the pretraining technology @xcite , state-of-the-art models have been shown comparative with human per-figure 1 : answering a multi-hop question over the relation graph. the relations are constrained predicates in the label form (i.e., knowledge graph) while free texts in the text form. the reasoning process has been marked in the graph, where the correspondence between relations and question words has been highlighted in the same color. in this paper, we focus on multi-hop qa based on relation graphs, which consists of entities and their relations. as shown in figure 1 , the relations can be represented by two forms: in this paper, we aim to tackle multi-hop questions over these two different forms in a unified framework. existing methods for multi-hop qa have two main strands. the first is to predict the sequential relation path in a weakly supervised setting @xcite , that is, to learn the intermediate path only based on the final answer. these works suffer from the convergence issues due to the huge search space, which heavily hinders their performance. besides, they are mostly proposed for the label form. so, it is not clear how to adapt them to the text form, whose search space is even much huger. the second strand is to collect evidences by using graph neural networks @xcite . they can handle both the two relation forms and achieve state-of-the-art performance. although they prevail over the path-based models in performance, they are weak in interpretability since their intermediate reasoning process is black-box neural network layers. in this paper, we propose a novel model for multi-hop qa, dubbed transfernet, which has the following advantages: 1) generality. it can deal with the label form, the text form, and their combinations in a unified framework. 2) effectiveness. transfernet outperforms previous models significantly, achieving 100% accuracy of 2-hop and 3-hop questions in metaqa dataset. 3) transparency. transfernet is fully attention-based, so its intermediate steps can be easily visualized and understood by humans. specifically, transfernet infers the answer by transfering entity scores along relation scores of multiple steps. it starts from the topic entity of the question and maintains an entity score vector, whose elements indicate the probability of an entity being activated. at each step, it attends to some question words (e.g., the wife of ) and compute scores for the relations in the graph. relations relevant to the question words will have high scores (e.g., spouse). we formulate these relation scores into an adjacent matrix, where each entry indicates the transfer probability of an entity pair. by multiplying the entity score vector with the relation score matrix, we can "hop" along relations in a differentiable manner. after repeating for multiple steps, we can finally arrive at the target entity. we conduct experiments for the two forms respectively. for the label form, we use metaqa @xcite , webqsp @xcite and compwebq @xcite . transfernet achieves 100% accuracy in the 2-hop and 3-hop questions of metaqa. on webqsp and compwebq, we also achieve a significant improvement over state-of-the-art models. for the text form, following @xcite , we construct the relation graph of metaqa from the wikimovies corpus @xcite . we demonstrate that transfernet surpasses previous models by a large margin, especially for the 2-hop and 3-hop questions. when we mix the label form and the text form, transfernet still keeps its superiority. moreover, by visualizing the intermediate results, we show its strong interpretability. foot_0 ## related work in this paper we focus on multi-hop question answering over the graph structure that is either knowledge graph or built from text corpus. in previous works, graftnet @xcite and pullnet @xcite have a similar setting to ours but they mostly aim at the mixed form, which includes both label relations and text relations. they first retrieve a question-specific subgraph and then use graph convolutional networks @xcite to implicitly infer the answer entity. these gcn-based methods are usually weak in interpretability because they cannot produce the intermediate reasoning path, which is necessary in our opinion for the task of multi-hop question answering. besides, there are many works specifically for only one graph form: for the label form, which is also known as "kbqa" or "kgqa", existing methods fall into two categories: information retrieval @xcite @xcite and semantic parsing @xcite @xcite @xcite . the former retrieves answer from kg by learning representations of question and graph, while the latter queries answer by parsing the question into logical form. among these methods, vrn @xcite and srn @xcite have a good interpretability as they learn an explicit reasoning path with reinforcement learning. however, they suffer from the convergency issue due to the huge search space. irn @xcite and @xcite ) learn a soft distribution for intermediate relations and can be optimized using only the final answer. however, it is not clear how to extend them to the text form. question answering over text corpus is also known as "reading comprehension". for simple questions, whose answer can be retrieved directly from the text, pretrained models @xcite have performed better than humans @xcite . for multi-hop questions that are much more challenging, existing works @xcite @xcite usually convert the text into a rule-based or learning-based entity graph, and then use graph neural networks @xcite to perform implicit reasoning. similar to pullnet, they are weak in interpretability. besides, most of them build the graph by just connecting relevant entities, missing the important edge textual information. ## conclusions we proposed transfernet, an effective and transparent framework for multi-hop qa over knowledge graph or text-formed relation graph. it achieved 100% accuracy on 2-hop and 3-hop questions of label-formed metaqa, nearly solving the dataset. on the more challenging webqsp, compwebq and text-formed metaqa, it also outperforms other state-of-the-art models significantly. qualitative analysis shows the good interpretability of trans-fernet.