Proceedings of NAACL-HLT 2019, pages 3016–3025Minneapolis, Minnesota, June 2 - June 7, 2019. c©2019 Association for Computational Linguistics

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Long-tail Relation Extraction via Knowledge Graph Embeddings andGraph Convolution Networks

Ningyu Zhang1,2,3 Shumin Deng1,3 Zhanlin Sun1,3 Guanying Wang1,3

Xi Chen4 Wei Zhang2,3 Huajun Chen1,3∗

1. College of Computer Science and Technology, Zhejiang University2. Alibaba Group

3. AZFT†Joint Lab for Knowledge Engine4. Tencent

{3150105645,231sm,guanying wgy,huajunsir}@zju.edu.cnjasonxchen@tencent.com,{ningyu.zny,lantu.zw}@alibaba-inc.com

Abstract

We propose a distance supervised relation ex-traction approach for long-tailed, imbalanceddata which is prevalent in real-world settings.Here, the challenge is to learn accurate ”few-shot” models for classes existing at the tail ofthe class distribution, for which little data isavailable. Inspired by the rich semantic cor-relations between classes at the long tail andthose at the head, we take advantage of theknowledge from data-rich classes at the headof the distribution to boost the performance ofthe data-poor classes at the tail. First, we pro-pose to leverage implicit relational knowledgeamong class labels from knowledge graph em-beddings and learn explicit relational knowl-edge using graph convolution networks. Sec-ond, we integrate that relational knowledgeinto relation extraction model by coarse-to-fine knowledge-aware attention mechanism.We demonstrate our results for a large-scalebenchmark dataset which show that our ap-proach significantly outperforms other base-lines, especially for long-tail relations.

1 Introduction

Relation extraction (RE) is an important task in in-formation extraction, aiming to extract the relationbetween two given entities based on their relatedcontext. Due to the capability of extracting tex-tual information and benefiting many NLP appli-cations (e.g., information retrieval, dialog genera-tion, and question answering), RE appeals to manyresearchers. Conventional supervised models havebeen widely explored in this task (Zelenko et al.,2003; Zeng et al., 2014); however, their perfor-mance heavily depends on the scale and qualityof training data.

∗ Corresponding author.†Alibaba-Zhejiang University Frontier Technology Re-

search Center

0 10 20 30 40 50Sorted Relation ID

0

10000

20000

30000

40000

50000

60000

70000

Rela

tion

Labe

l Fre

quen

cy

~40 relation labels occur <1000 times

Figure 1: Label frequency distribution of classes with-out NA in NYT dataset.

To construct large-scale data, (Mintz et al.,2009) proposed a novel distant supervision (DS)mechanism to automatically label training in-stances by aligning existing knowledge graphs(KGs) with text. DS enables RE models to workon large-scale training corpora and has thus be-come a primary approach for RE recently (Wuet al., 2017; Feng et al., 2018). Although theseDS models achieve promising results on commonrelations, their performance still degrades dramat-ically when there are only a few training instancesfor some relations. Empirically, DS can automat-ically annotate adequate amounts of training data;however, this data usually only covers a limitedpart of the relations. Many relations are long-tailand still suffer from data deficiency. Current DSmodels ignore the problem of long-tail relations,which makes it challenging to extract comprehen-sive information from plain text.

Long-tail relations are important and cannot beignored. Nearly 70% of the relations are long-tail in the widely used New York Times (NYT)dataset1 (Riedel et al., 2010; Lei et al., 2018) asshown in Figure 1. Therefore, it is crucial for mod-

1http://iesl.cs.umass.edu/riedel/ecml/

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els to be able to extract relations with limited num-bers of training instances.

Dealing with long tails is very difficult asfew training examples are available. There-fore, it is natural to transfer knowledge fromdata-rich and semantically similar head classesto data-poor tail classes (Wang et al., 2017).For example, the long-tail relation /peo-ple/deceased person/place of burial and headrelation /people/deceased person/place of deathare in the same branch /people/deceased person/*as shown in Figure 2. They are semantically simi-lar, and it is beneficial to leverage head relationalknowledge and transfer it to the long-tail relation,thus enhancing general performance. In otherwords, long-tail relations of one entity tuple canhave class ties with head relations, which can beleveraged to enhance RE for narrowing potentialsearch spaces and reducing uncertainties betweenrelations when predicting unknown relations (Yeet al., 2017). If one pair of entities contains/people/deceased person/place of death, thereis a high probability that it will contain /peo-ple/deceased person/place of burial. If we canincorporate the relational knowledge between tworelations, extracting head relations will provideevidence for the prediction of long-tail relations.

However, there exist two problems: (1) Learn-ing relational knowledge: Semantically simi-lar classes may contain more relational informa-tion that will boost transfer, whereas irrelevantclasses (e.g., /location/location/contains and /peo-ple/family/country) usually contain less relationalinformation that may result in negative transfer.(2) Leveraging relational knowledge: Integrat-ing relational knowledge to existing RE models ischallenging.

To address the problem of learning relationalknowledge, as shown in (Lin et al., 2016; Yeet al., 2017), we use class embeddings to rep-resent relation classes and utilize KG embed-dings and graph convolution networks (GCNs)to extract implicit and explicit relational knowl-edge. Specifically, previous studies (Yang et al.,2015) have shown that the embeddings of se-mantically similar relations are located neareach other in the latent space. For instance,the relation /people/person/place lived and /peo-ple/person/nationality are more relevant, whereasthe relation /people/person/profession has less cor-relation with the former two relations. Thus, it

[ismail_merchant], whose filmmaking collaboration with james ivory created a genre of films with visually sumptuous settings that told literate tales of individuals trying to adapt to shifting societal values , died yesterday in a [London] hospital

[darren_mcgavin] , an actor with hundreds of television , movie and theatrical credits to his name , died on saturday in [los_angeles] .

the night the news hit that [hunter_s._Thompson] had committed suicide at his home in [woody_creek] , colo. , i drove to my office and read a few of the letters we had exchanged over the years .

noting that [charles_Darwin] is buried in [westminster_abbey ], dr. barrow said that in contrast with the so-called culture wars in america , science and religion had long coexisted peaceably in england . ''

/people/deceased_person/place_of_burial

/people/deceased_person/place_of_death

Long-tail relation (24 samples)

Head relation (2541 samples)

/people/deceased_person/*

Knowledge Transfer

Figure 2: Head and long-tail relations.

is natural to leverage this knowledge from KGs.However, because there are many one-to-multiplerelations in KGs, the relevant information for eachclass may be scattered. In other words, there maynot be enough relational signal between classes.Therefore, we utilize GCNs to learn explicit rela-tional knowledge.

To address the problem of leveraging relationalknowledge, we first use convolution neural net-works (Zeng et al., 2014, 2015) to encode sen-tences; then introduce coarse-to-fine knowledge-aware attention mechanism for combining rela-tional knowledge with encoded sentences into bagrepresentation vectors. The relational knowledgenot only provides more information for relationprediction but also provides a better referencemessage for the attention module to raise the per-formance of long-tail classes.

Our experimental results on the NYT datasetshow that: (1) our model is effective comparedto baselines especially for long-tail relations; (2)leveraging relational knowledge enhances RE andour model is efficient in learning relational knowl-edge via GCNs.

2 Related Work

Relation Extraction. Supervised RE models (Ze-lenko et al., 2003; GuoDong et al., 2005; Mooneyand Bunescu, 2006) require adequate amountsof annotated data for training which is time-consuming. Hence, (Mintz et al., 2009) proposdDS to automatically label data. DS inevitably ac-companies with the wrong labeling problem. Toalleviate the noise issue, (Riedel et al., 2010; Hoff-mann et al., 2011) proposed multi-instance learn-ing (MIL) mechanisms. Recently, neural mod-els have been widely used for RE; those mod-els can accurately capture textual relations with-out explicit linguistic analysis (Zeng et al., 2015;

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Lin et al., 2016; Zhang et al., 2018a). To fur-ther improve the performance, some studies incor-porate external information (Zeng et al., 2017; Jiet al., 2017; Han et al., 2018a) and advanced train-ing strategies (Ye et al., 2017; Liu et al., 2017;Huang and Wang, 2017; Feng et al., 2018; Zenget al., 2018; Wu et al., 2017; Qin et al., 2018).These works mainly adopt DS to make large-scaledatasets and reduce the noise caused by DS, re-gardless of the effect of long-tail relations.

There are only a few studies on long-tail forRE (Gui et al., 2016; Lei et al., 2018; Han et al.,2018b). Of these, (Gui et al., 2016) proposed anexplanation-based approach, whereas (Lei et al.,2018) utilized external knowledge (logic rules).These studies treat each relation in isolation, re-gardless of the rich semantic correlations betweenthe relations. (Han et al., 2018b) proposed ahierarchical attention scheme for RE, especiallyfor long-tail relations. Different from those ap-proaches, we leverage implicit and explicit rela-tional knowledge from KGs and GCNs rather thandata-driven learned parameter spaces where simi-lar relations may have distinct parameters, hinder-ing the generalization of long-tail classes.

Knowledge Graph Embedding. Recently,several KG embedding models have been pro-posed. These methods learn low-dimensional vec-tor representations for entities and relations (Bor-des et al., 2013; Wang et al., 2014; Lin et al.,2015). TransE (Bordes et al., 2013) is one ofthe most widely used models, which views rela-tions as translations from a head entity to a tail en-tity on the same low-dimensional hyperplane. In-spired by the rich knowledge in KGs, recent works(Han et al., 2018a; Wang et al., 2018; Lei et al.,2018) extend DS models under the guidance ofKGs. However, these works neglect rich corre-lations between relations. Relation structure (re-lational knowledge) has been studied and is quiteeffective for KG completion (Zhang et al., 2018b).To the best of our knowledge, this is the first effortto consider the relational knowledge of classes (re-lations) using KGs for RE.

Graph Convolutional Networks. GCNs gen-eralize CNNs beyond two-dimensional and one-dimensional spaces. (Defferrard et al., 2016)developed spectral methods to perform efficientgraph convolutions. (Kipf and Welling, 2016) as-sumed the graph structure is known over inputinstances and apply GCNs for semi-supervised

learning. GCNs were applied to relational data(e.g., link prediction) by (Schlichtkrull et al.,2018). GCNs have also had success in other NLPtasks such as semantic role labeling (Marcheg-giani and Titov, 2017), dependency parsing(Strubell and McCallum, 2017), and machinetranslation (Bastings et al., 2017).

Two GCNs studies share similarities with ourwork. (1) (Chen et al., 2017) used GCNs on struc-tured label spaces. However, their experimentsdo not handle long-tail labels and do not incorpo-rate attention but use an average of word vectorsto represent each document. (2) (Rios and Kavu-luru, 2018) proposed a few-shot and zero-shot textclassification method by exploiting structured la-bel spaces with GCNs. However, they used GCNsin the label graph whereas we utilize GCNs in thehierarchy graph of labels.

3 Methodology

In this section, we introduce the overall frameworkof our approach for RE, starting with the notations.

3.1 Notations

We denote a KG as G = E ,R,F , where E , R andF indicate the sets of entities, relations and factsrespectively. (h, r, t) ∈ F indicates that there isa relation r ∈ R between h ∈ E and t ∈ E . Wefollow the MIL setting and split all instances intomultiple entity-pair bags {Sh1,t1 ,Sh2,t2 , ...}. Eachbag Shi,ti contains multiple instances {s1, s2, ...}mentioning both entities hi and ti. Each instances in these bags is denoted as a word sequence s ={w1, w2, ...}.

3.2 Framework

Our model consists of three parts as shown in Fig-ure 3:

Instance Encoder. Given an instance and itsmentioned entity pair, we employ neural networksto encode the instance semantics into a vector.In this study, we implement the instance encoderwith convolutional neural networks (CNNs) givenboth model performance and time efficiency.

Relational Knowledge Learning. Given pre-trained KG embeddings (e.g., TransE (Bordeset al., 2013)) as implicit relational knowledge, weemploy GCNs to learn explicit relational knowl-edge. By assimilating generic message-passing in-ference algorithms with neural-network counter-part, we can learn better embeddings for Knowl-

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Figure 3: Architecture of our proposed model.

edge Relation. We concatenate the outputs ofGCNs and the pretrained KG embeddings to formthe final class embeddings.

Knowledge-aware Attention. Under the guid-ance of final class embeddings, knowledge-awareattention is aimed to select the most informativeinstance exactly matching relevant relation.

3.3 Instance Encoder

Given an instance s = {w1, ..., wn} mentioningtwo entities, we encode the raw instance into acontinuous low-dimensional vector x, which con-sists of an embedding layer and an encoding layer.

Embedding Layer. The embedding layer isused to map discrete words in the instance intocontinuous input embeddings. Given an instances, we map each word wi in the instance to areal-valued pretrained Skip-Gram (Mikolov et al.,2013) embedding wi ∈ Rdw . We adopt positionembeddings following (Zeng et al., 2014). Foreach word wi, we embed its relative distances tothe two entities into two dp-dimensional vectors.We then concatenate the word embeddings and po-sition embeddings to achieve the final input em-beddings for each word and gather all the inputembeddings in the instance. We thus obtain an em-bedding sequence ready for the encoding layer.

Encoding Layer. The encoding layer aimsto compose the input embeddings of a given in-stance into its corresponding instance embedding.In this study, we choose two convolutional neu-ral architectures, CNN (Zeng et al., 2014) andPCNN (Zeng et al., 2015) to encode input em-

beddings into instance embeddings. Other neu-ral architectures such as recurrent neural networks(Zhang and Wang, 2015) can also be used as sen-tence encoders. Because previous works show thatboth convolutional and recurrent architectures canachieve comparable state-of-the-art performance,we select convolutional architectures in this study.Note that, our model is independent of the encoderchoices, and can, therefore, be easily adapted to fitother encoder architectures.

3.4 Relational Knowledge Learning throughKG Embeddings and GCNs.

Given pretrained KG embeddings and predefinedclass (relation) hierarchies2, we first leverage theimplicit relational knowledge from KGs and ini-tialize the hierarchy label graph; then we applytwo layer GCNs to learn explicit fine-grained re-lational knowledge from the label space.

Hierarchy Label Graph Construction. Givena relation set R of a KG G (e.g., Freebase),which consists of base-level relations (e.g., /peo-ple/person/ethnicity), we can generate the corre-sponding higher-level relation set RH . The re-lations in a high-level set (e.g., people) are moregeneral and common; they usually contain severalsub-relations in the base-level set. The relationhierarchies are tree-structured, and the generationprocess can be done recursively. We use a virtualfather node to construct the highest level associa-

2For datasets without predefined relation hierarchies, hi-erarchy clustering (Johnson, 1967) or K-means can constructrelation hierarchies (Zhang et al., 2018b); details can befound in supplementary materials.

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tions between relations as shown in Figure 3. Inpractice, we start from R0 = R which is the setof all relations we focus on for RE, and the gener-ation process is performed L − 1 times to get thehierarchical relation sets {R0,R1, ...,RL}, whereRL is the virtual father node. Each node hasa specific type t ∈ {0, 1, ..., L} to identify itslayer hierarchies. For example, as shown in Fig-ure 3, node /people/person/ethnicity has a spe-cific type 0 to indicate it is in the bottom layer ofthe graph. The vectors of each node in the bot-tom layer are initialized through pretrained TransE(Bordes et al., 2013) KG embeddings. Other KGembeddings such as TransR (Lin et al., 2015) canalso be adopted. Their parent nodes are initializedby averaging all children vectors. For example,the node vector of /people/person/ is initialized byaveraging all the nodes under the branch of /peo-ple/person/* (all child nodes).

GCN Output Layer. Due to one-to-multiplerelations and incompleteness in KGs, the implicitrelevant information obtained by KG embeddingsfor each label is not enough. Therefore, we ap-ply GCNs to learn explicit relational knowledgeamong labels. We take advantage of the structuredknowledge over our label space using a two-layerGCNs. Starting with the pretrained relation em-bedding vimplicit

i ∈ Rd from KGs, we combinethe label vectors of the children and parents forthe i-th label to form,

v1i = f(W 1vi +∑j∈Np

W 1p vj

|Np|+

∑j∈Nc

W 1c vj|Nc|

+ b1g)

(1)where W 1 ∈ Rq∗d, W 1

p ∈ Rq∗d, W 1c ∈ Rq∗d,

b1g ∈ Rq, f is the rectified linear unit (Nair andHinton, 2010) function, and Nc (Np) is the indexset of the i-th labels children (parents). We usedifferent parameters to distinguish each edge typewhere parent edges represent all edges from highlevel labels and child edges represent all edgesfrom low level labels. The second layer followsthe same formulation as the first layer and outputsvexpliciti . Finally, we concatenate both pretrainedvimpliciti with GCNs node vector vexpliciti to form

hierarchy class embeddings,

qr = vimpliciti ||vexpliciti (2)

where qr ∈ Rd+q.

3.5 Knowledge-aware Attention

Traditionally, the output layer of PCNN/CNNwould learn label specific parameters optimizedby a cross-entropy loss. However, the label spe-cific parameters spaces are unique to each relation,matrices associated with the long-tails can only beexposed to very few facts during training, result-ing in poor generalization. Instead, our methodattempts to match sentence vectors to their corre-sponding class embeddings rather than learning la-bel specific attention parameters. In essence, thisbecomes a retrieval problem. Relevant informa-tion from class embeddings contains useful rela-tional knowledge for long-tails among labels.

Practically, given the entity pair (h, t) and itsbag of instances Sh,t = {s1, s2, ..., sm}, weachieve the instance embeddings {s1, s2, ..., sm}using the sentence encoder. We group the classembeddings according to their type (i.e., accord-ing to their layers in the hierarchy label graph),e.g., qri , i ∈ {0, 1, ..., L}. We adopt qri , i 6= L(layer L is the virtual father node) as layer-wiseattention query vector. Then, we apply coarse-to-fine knowledge-aware attention to them to obtainthe textual relation representation rh,t. For a rela-tion r, we construct its hierarchical chain of par-ent relations (r0, ..., rL−1) using the hierarchy la-bel graph, where ri−1 is the sub-relation of ri. Wepropose the following formulas to compute the at-tention weight (similarity or relatedness) betweeneach instances feature vector sk and qri ,

ek =Ws(tanh[sk; qri ]) + bs

αik =

exp(ek)∑mj=1 exp(ej)

(3)

where [x1;x2] denotes the vertical concatenationof x1 and x2, Ws is the weight matrix, and bsis the bias. We compute attention operations oneach layer of hierarchy label graph to obtain cor-responding textual relation representations,

rih,t = ATT (qri , {s1, s2, ..., sm}) (4)

Then we need to combine the relation represen-tations on different layers. Direct concatenation ofall the representations is a straightforward choice.However, different layers have different contribu-tions for different tuples. For example, the relation/location/br state/ has only one sub-relation /loca-tion/br state/capital, which indicates that it is moreimportant. In other words, if the sentence has high

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attention weights on relation /location/br state/, ithas a very high probability to have relation /loca-tion/br state/capital. Hence, we use an attentionmechanism to emphasize the layers,

gi =Wgtanh(rh,t)

βi =exp(gi)∑L−1

j=0 exp(gj)

(5)

where Wg is a weight matrix, rh,t is referred toas a query-based function that scores how well theinput textual relation representations and the pre-dict relation r match. The textual relation repre-sentations in each layer are computed as,

rih,t = βirih,t (6)

We simply concatenate the textual relation repre-sentations on different layers as the final represen-tation,

rh,r = Concat(r0h,t, .., rL−1h,t ) (7)

The representation rh,t will be finally fed to com-pute the conditional probability P(r|h, t,Sh,t),

P(r|h, t,Sh,t) =exp(or)∑r∈R exp(or)

(8)

where o is the score of all relations defined as,

o =Mrh,t (9)

where M is the representation matrix to calculatethe relation scores. Note that, attention weight qriis obtained from the outputs of GCNs and pre-trained KG embeddings, which can provide moreinformative parameters than data-driven learnedparameters, especially for long-tails.

4 Experiments

4.1 Datasets and EvaluationWe evaluate our models on the NYT dataset de-veloped by (Riedel et al., 2010), which has beenwidely used in recent studies (Lin et al., 2016; Liuet al., 2017; Wu et al., 2017; Feng et al., 2018).The dataset has 53 relations including the NArelation, which indicates that the relations of in-stances are not available. The training set has522611 sentences, 281270 entity pairs, and 18252relational facts. In the test set, there are 172448sentences, 96678 entity pairs, and 1950 relationalfacts. In both training and test set, we truncate sen-tences with more than 120 words into 120 words.

Figure 4: Precision-recall curves for the proposedmodel and various baseline models.

Figure 5: Precision-recall curves for the proposedmodel and various attention-based neural models.

We evaluate all models in the held-out evalua-tion. It evaluates models by comparing the rela-tional facts discovered from the test articles withthose in Freebase and provides an approximatemeasure of precision without human evaluation.For evaluation, we draw precision-recall curvesfor all models. To further verify the effect of ourmodel for long-tails, we follow previous studies(Han et al., 2018b) to report the Precision@N re-sults. The dataset and baseline code can be foundon Github 3.

4.2 Parameter Settings4

To fairly compare the results of our models withthose baselines, we also set most of the experimen-tal parameters by following (Lin et al., 2016). Weapply dropout on the output layers of our modelsto prevent overfitting. We also pretrain the sen-tence encoder of PCNN before training our model.

3https://github.com/thunlp/OpenNRE4Details of hyper-parameters settings and evaluation of

different instances can be found in supplementary materials

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Training InstancesHits@K (Macro)

<100 <20010 15 20 10 15 20

CNN+ATT <5.0 <5.0 18.5 <5.0 16.2 33.3

+HATT 5.6 31.5 57.4 22.7 43.9 65.1+KATT 9.1 41.3 58.5 23.3 44.1 65.4

PCNN+ATT <5.0 7.4 40.7 17.2 24.2 51.5

+HATT 29.6 51.9 61.1 41.4 60.6 68.2+KATT 35.3 62.4 65.1 43.2 61.3 69.2

Table 1: Accuracy (%) of Hits@K on relations withtraining instances fewer than 100/200.

4.3 Overall Evaluation Results

To evaluate the performance of our proposedmodel, we compare the precision-recall curvesof our model with various previous RE models.The evaluation results are shown in Figure 4 andFigure 5. We report the results of neural ar-chitectures including CNN and PCNN with var-ious attention based methods: +KATT denotesour approach, +HATT is the hierarchical atten-tion method (Han et al., 2018b), +ATT is theplain selective attention method over instances(Lin et al., 2016), +ATT+ADV is the denoisingattention method by adding a small adversarialperturbation to instance embeddings (Wu et al.,2017), and +ATT+SL is the attention-based modelusing soft-labeling method to mitigate the sideeffect of the wrong labeling problem at entity-pair level (Liu et al., 2017). We also compareour method with feature-based models, includingMintz (Mintz et al., 2009), MultiR (Hoffmannet al., 2011) and MIML (Surdeanu et al., 2012).

As shown in both figures, our approach achievesthe best results among all attention-based mod-els. Even when compared with PCNN+HATT,PCNN+ATT+ADV, and PCNN+ATT+SL, whichadopt sophisticated denoising schemes and extrainformation, our model is still more advantageous.This indicates that our method can take advantageof the rich correlations between relations throughKGs and GCNs, which improve the performance.We believe the performance of our model can befurther improved by adopting additional mecha-nisms like adversarial training, and reinforcementlearning, which will be part of our future work.

4.4 Evaluation Results for Long-tailRelations

To further demonstrate the improvements in per-formance for long-tail relations, following thestudy by (Han et al., 2018b) we extract a subsetof the test dataset in which all the relations have

Training InstancesHits@K (Macro)

<100 <20010 15 20 10 15 20

+KATT 35.3 62.4 65.1 43.2 61.3 69.2w/o hier 34.2 62.1 65.1 42.5 60.2 68.1

w/o GCNs 30.5 61.9 63.1 39.5 58.4 66.1Word2vec 30.2 62.0 62.5 39.6 57.5 65.8w/o KG 30.0 61.0 61.3 39.5 56.5 62.5

Table 2: Results of ablation study with PCNN.

fewer than 100/200 training instances. We employthe Hits@K metric for evaluation. For each en-tity pair, the evaluation requires its correspondinggolden relation in the first K candidate relationsrecommended by the models. Because it is diffi-cult for the existing models to extract long-tail re-lations, we select K from {10,15,20}. We reportthe macro average Hits@K accuracies for thesesubsets because the micro-average score generallyoverlooks the influences of long-tails. From theresults shown in Table 1, we observe that for bothCNN and PCNN models, our model outperformsthe plain attention model and the HATT model.Although our KATT method has achieved betterresults for long-tail relations as compared to bothplain ATT method and HATT method, the resultsof all these methods are still far from satisfactory.This indicates that distantly supervised RE mod-els still suffer from the long-tail relation problem,which may require additional schemes and extrainformation to solve this problem in the future.

4.5 Ablation Study

To analyze the contributions and effects of differ-ent technologies in our approach, we perform ab-lation tests. +KATT is our method; w/o hier isthe method without coarse-to-fine attention (onlyutilizes bottom node embeddings of the hierarchylabel graph), which implies no knowledge trans-fer from its higher level classes; w/o GCN is themethod without GCNs, which implies no explicitrelational knowledge; Word2vec is the method inwhich the node is initialized with pretrained Skip-Gram (Mikolov et al., 2013) embeddings; and w/oKG is the method in which the node is initial-ized with random embeddings, which implies noprior relational knowledge from KGs. From theevaluation results in Table 2, we observe that theperformance slightly degraded without coarse-to-fine attention, which proves that knowledge trans-fer from the higher node is useful. We also no-ticed that the performance slightly degraded with-out KG or using word embeddings, and the perfor-

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(a) HATT (b) +KG (c) +GCNs (d) KATT

Figure 6: T-SNE visualizations of class embeddings. Cluster in the upper right is the relation /location/*/* andcluster in the bottom left is the relation /people/*/* ). The square, triangle, and star refer to the high (/location)middle (/location/location/) and base (/location/location/contains) level relations, respectively.

mance significantly degraded when we removedGCNs. This is reasonable because GCNs can learnmore explicit correlations between relation labels,which boost the performance for long-tail rela-tions.

/people/deceased person/place of burial HATT KATTrichard wagner had his bayreuth, withits festspielhaus specially designed to ac-commodate his music dramas.

0.21 0.07

wotan and alberich are richard wagner;and the rheingold and valhalla are wag-ner’s real-life grail, the opera house inbayreuth.

0.15 0.13

/location/br state/capital HATT KATTthere’s violence everywhere, said ms.mesquita, who, like her friend, livesin belo horizonte, the capital of mi-nas gerais state

0.47 0.51

all the research indicates that we are cer-tain to find more gas in th amazon, ed-uardo braga, the governor of amazonas,said in an interview in manaus

0.46 0.45

Table 3: Example sentences for case study.

4.6 Case Study

We give some examples to show how ourmethod affects the selection of sentences. InTable 3, we display the sentence’s attentionscore in the lowest level5. Both the relation/people/deceased person/place of burial (24 in-stances) and /location/br state/capital (4 instances)are long-tail relations. On one hand, relation /peo-ple/deceased person/place of burial has semanti-cally similar data-rich relation such as /peo-ple/deceased person/place of death. We observethat HATT erroneously assigns high attention tothe incorrect sentence whereas KATT successfullyassigns the right attention weights, which demon-

5Both HATT and KATT methods can successfully selectthe correct sentence at the higher-level; details can be foundin supplementary materials.

strates the efficacy of knowledge transfer fromsemantically similar relations (Both HATT andKATT methods can take advantage of knowledgetransfer of high-level relations.). On the otherhand, the relation /location/br state/capital doesnot have semantically similar relations. However,we notice that KATT still successfully assigns theright attention weights, which demonstrates the ef-ficacy of knowledge transfer from high-level rela-tions using coarse-to-fine knowledge-aware atten-tion.

4.7 Visualizations of Class Embeddings

We visualize the class embeddings via t-SNE(Maaten and Hinton, 2008) to further show howGCNs and KG embeddings can help RE for long-tail relations. We observe that (1) Figure 6(a)and 6(d) show that semantically similar class em-beddings are closer with GCNs and pretrainedKG embeddings, which help select long-tail in-stances. (2) Figure 6(b) and 6(c) show thatKG embeddings and GCNs have different con-tributions for different relations to learn rela-tional knowledge between classes. For example,/location/location/contain has a sparse hierarchystructure, which leads to inefficient learning forGCNs; therefore, the relative distance changesonly slightly, which reveals the necessity of im-plicit relational knowledge from KGs. (3) Figure6(d) shows that there are still some semanticallysimilar class embeddings located far away, whichmay degrade the performance for long-tails. Thismay be caused by either sparsity in the hierarchylabel graph or equal treatment for nodes with thesame parent in GCNs, which is not a reasonablehypothesis. We will address this by integratingmore information such as relation descriptions orcombing logic reasoning as a part of future work.

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5 Conclusion and Future Work

In this paper, we take advantage of the knowledgefrom data-rich classes at the head of distributionto boost the performance of the data-poor classesat the tail. As compared to previous methods, ourapproach provides fine-grained relational knowl-edge among classes using KG and GCNs, whichis quite effective and encoder-agnostic.

In the future, we plan to explore the follow-ing directions: (1) We may combine our methodwith recent denoising methods to further improveperformance. (2) We may combine rule miningand reasoning technologies to learn better classembeddings to boost performance. (3) It will bepromising to apply our method to zero-shot REand further adapt to other NLP scenarios.

Acknowledgments

We want to express gratitude to the anony-mous reviewers for their hard work and kindcomments, which will further improve ourwork in the future. This work is fundedby NSFC91846204/61473260, national key re-search program YS2018YFB140004, AlibabaCangJingGe (Knowledge Engine) Research Planand Natural Science Foundation of ZhejiangProvince of China (LQ19F030001).

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