A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation (2021)

Abstract: Multi-hop relation detection in Knowledge Base Question Answering (KBQA) aims at retrieving the relation path starting from the topic entity to the answer node based on a given question, where the relation path may comprise multiple relations. Most of the existing methods treat it as a single-label learning problem while ignoring the fact that for some complex questions, there exist multiple correct relation paths in knowledge bases. Therefore, in this paper, multi-hop relation detection is considered as a multi-label learning problem. However, performing multi-label multi-hop relation detection is challenging since the numbers of both the labels and the hops are unknown. To tackle this challenge, multi-label multi-hop relation detection is formulated as a sequence generation task. A relation-aware sequence relation generation model is proposed to solve the problem in an end-to-end manner. Experimental results show the effectiveness of the proposed method for relation detection and KBQA.

## introduction with the development of knowledge bases (kbs) such as dbpedia, freebase, and wikidata, knowledge base question answering (kbqa) system @xcite @xcite is attracting more and more attention. the kbqa system often contains two core components: (1) entity linking, which identifies the topic entity mentioned in the question; (2) relation detection, which detects the relation paths starting from the topic entity to the answer node. relation detection in kbqa can be categorized into singe-relation (one-hop) detection and multirelation (multi-hop) detection. most existing singlerelation detection methods @xcite @xcite ., * corresponding author. what was pierce brosnan's first outing as 007? film/actor/film film/film_actor/portrayed_in_film which city hosted the 1900 summer olympics? olympic/olympic_games/host_city time/time/event 2018) rely on measuring the semantic similarity of questions and candidate relations. @xcite proposed an encoder-decoder based generative framework for single-relation extraction. for multi-relation detection, some approaches @xcite @xcite proposed to tackle two or three-relation detection by applying some constraint which makes the number of hops fixed. xiong et al. (2017 however, for some question, there may exist multiple relation paths to the correct answer. for example, as shown in the upper part of figure 1 , there are two distinct relation paths time/time/event and olympic/olymipic_games/host_city with the same meaning, making the instance multi-label. moreover, as shown in the lower part of figure 1 , there are two relation paths starting from different topic entities pierce brosnan and 007. a robust kbqa system should be able to infer the final answer based on multiple relation paths. we therefore consider multi-label multi-hop relation detection in this paper. nevertheless, it is challenging to perform multi-label multi-hop relation detection since both the number of relation paths and the number of hops in each relation path are unknown. to deal with such a challenge, in this paper, we formulate it as a sequence generation task in the following form {r 1 1 , . . . , r 1 n 1 ,[sep], r 2 1 , . . . , r 2 n 2 ,[sep],r m 1 , . . . , r m nm ,[end]}, where r j i denotes the i-th relation in j-th path, the comma splits the relation chains and the [sep] indicates the division of different relation paths. a relation-aware sequence generation model (rsgm) is proposed to learn the sequence generation task end-to-end, without the need of knowing the number of labels and hops beforehand. in specific, a pre-trained bidirectional encoder representations from transformers (bert) model is employed as the encoder of rsgm while a gated recurrent unit (gru) with relation-aware attention is design as the decoder to incorporating the semantic information of the relations. moreover, a constraint-learning strategy is proposed to mitigate the exposure bias and label repetition problem in sequence generation. the main contributions of this paper are: ## experiments we conduct experiments on a large kbqa benchmark dataset freebaseqa @xcite to evaluate the effectiveness of the proposed rsgm model. ## conclusion in this paper, we frame multi-hop relation detection as a multi-label learning problem. to solve the challenge of multi-label multi-hop relation detection, we cast it as a sequence generation problem. a relation-aware sequence relation generation model is proposed to learn the problem in an end-to-end manner. experimental results show that our approach not only achieves better relation detection performance, but also improves the results of the state-of-the-art kbqa system.