Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences. Efforts thus far have focused on improving extraction accuracy but little is known about their explainability. In this work we annotate a test set with ground-truth sentence-level explanations to evaluate the quality of explanations afforded by the relation extraction models. We demonstrate that replacing the entity mentions in the sentences with their fine-grained entity types not only enhances extraction accuracy but also improves explanation. We also propose to automatically generate distractor sentences to augment the bags and train the model to ignore the distractors. Evaluations on the widely used FB-NYT dataset show that our methods achieve new state-of-the-art accuracy while improving model explainability. Relation extraction with distant supervision associates a pair of entities with a bag of sentences, each containing mentions of both entities. The bag is tagged with relations between the pair in a Knowledge Base (KB), without explicitly indicating which sentence(s) support the relation(s). This method avoids the burden of manual annotations, but presents inherent ambiguity, creating challenges for learning. To alleviate the impact of the irrelevant sentences many approaches have been proposed including models based on attention (Zeng et al., 2015; Lin et al., 2016; Liu et al., 2017; Luo et al., 2017; Du et al., 2018; Wang et al., 2018; Peng and Denilson, 2019; Bai and Ritter, 2019), approaches that use additional resources (Vashishth et al., 2018; Liu et al., 2018) and methods that utilize supervision data (Pershina et al., 2014; Angeli et al., 2014; Beltagy et al., 2019). These studies primarily focus on improving relation extraction accuracy and little is known about whether the models are making right decision for the right reason or because of some irrelevant biases (Agrawal et al., 2016; Gururangan et al., 2018; Ghaeini et al., 2019). This paper examines two strong baseline relation extraction models with several explanation mechanisms. We manually annotated a test set from the widely used FB-NYT dataset with ground truth explanations to evaluate the quality of the explanation afforded by these models. We also introduce two different methods for improving relation extraction. First, we demonstrate that replacing the entity mentions with their fine-grained entity types for sentence representation leads to improvement in both the extract accuracy and model explainability. Second, we augment the bags with automatically generated distractor sentences (i.e., sentences that contain no supporting information for the relation) and train the model to appropriately ignore the irrelevant information. Our evaluation on the widely used FB-NYT dataset verifies that the proposed methods achieve the new state of the art for the extraction performance along with improved model explainability. Given entity pair ( e i , e j ) , we form a bag B i,j = { s 1 , . . . s N ij } with N ij sentences that contain mentions of both entities and label it by the set of relations between e i and e j from the KB. Neural models for relation extraction encode each sentences into a vector representation and a bag B i,j is thus represented by { x 1 , . . . x Nij } where x i  R d . Given a set of bags and the associated labels, the training objective is to learn a model that predicts the probability P ( r = k | B i,j ) that relation k exists between e i and e j based on B i,j , where k  1 . . . K and K is the total number of relations in the KB. There are zero to multiple possible relation labels for each bag. Importantly, only some sentences in the bag express any of the relations and the others are irrelevant (provide no information regarding the relations), but such sentences are not labeled. We consider two baselines. The first is DirectSup, a recent model achieving the state-of-the-art performance by utilizing auxiliary supervision (Beltagy et al., 2019). The second baseline (CNNs+ATT) revamps the classic attention based method by Lin et al. (2016) but adopts the same sentence encoder as DirectSup for ease of comparisons. In this work, we add a ReLU at the end of the sentence encoder (Beltagy et al., 2019) to produce positive sentence representations. See (Beltagy et al., 2019) for detailed information regarding the sentence encoder. DirectSup. Given a bag of sentences, DirectSup encodes each sentence using CNNs with different filter sizes. The outputs of the CNNs with different filter sizes are concatenated to produce the encoding of the sentence. Given a bag B and the encoding of its sentences { x 1 , x 2 , ..., x N } , DirectSup assigns an importance weight for each sentence based on the output of a binary classifier learned from an additional direct supervision data in a multi-task manner. Given a sentence encoding x n , the binary classifier provides a weight  n  [0 , 1] indicating the likelihood that x n expresses some form of relations in the KB. As a result, for a bag B i,j , we have importance weights {  1 , . . . ,  N } . It then produces a single bag representation as follows:  x = Max-pool ( {  1 x 1 , . . . ,  n x N } ) (1) and the prediction for relation k is given by: P ( r = k | B ) =  ( x  r k + b k ) (2) where r k is an embedding of relation k , b k is a bias variable and  is the Sigmoid function. CNNs+ATT. This model uses the same sentence encoder as DirectSup but differs in the attention mechanism used to decide sentence importance. Specifically, it follows Lin et al. (2016) and computes the importance weights of the sentences in bag B with encodings { x 1 , . . . , x N } as follows:  k,n = exp ( x n Aq k ) (cid:80) Ni =1 exp ( x i Aq k ) (3) where q k is a learned query vector associated with relation k and A is a diagonal matrix. Given {  k, 1 , ...,  k,N } , we compute a bag representation specific for relation k by:  x k = N (cid:88) n =1  k,n x n (4) and the prediction for relation k is given by: P ( r = k | B ) =  ( x k  r k + b k ) (5) where r k is relation k 's embedding and b k is the bias. Entity embedding. Prior work has demonstrated that incorporating entity embeddings into the relation extraction model leads to improved accuracy (Ji et al., 2017; Beltagy et al., 2019). Here we also consider this strategy with the baseline models. Specifically, let v i and v j be the entity embedding of e i and e j , we concatenate the bag representations  x with v i  v j and v i  v j , where  is element-wise product. We then apply a linear project layer with ReLU to produce a new bag representation for final prediction with Eq. 2 and 5. For any entity e i its embedding vector v i is obtained by concatenating the average of its skip-gram (Mikolov et al., 2013) word embeddings and the embeddings produced by Zhang et al. (2019) (produced by using TransE on Wikipedia factual tuples). Training objective. For all the models in this work we use the binary cross entropy loss function for training: l =  (cid:88) B i,j K (cid:88) k =1 1 i,j,k log P ( r = k | B i,j )+ (1  1 i,j,k ) log (1  P ( r = k | B i,j )) (6) where 1 i,j,k is an indicator function that takes value 1 if relation k exists for bag B i,j . The importance weights (  's, aka attention), generated by the models can be interpreted as explanations. However, recent studies (Ghaeini et al., 2018; Jain et al., 2019; Wiegreffe and Pinter, 2019) have questioned the validity of attention as a faithful explanation of model's behavior. Thus we consider the following additional explanation mechanisms: Saliency. Recent works show that a model's prediction can be explained by examining the input saliency, based on the gradient of the output w.r.t. the inputs (Simonyan et al., 2012; Ross et al., 2017; Ghaeini et al., 2019). We define the saliency of sentence n for relation k , denoted by S x n ,k , as the L1 norm of the gradient of relation k logit o k with respect to x n .(Appendix. A.1). Gradient  input. This is a commonly used measure for input attributions (Shrikumar et al., 2016; Selvaraju et al., 2019). We will refer to this measure as GI x n ,k , computed as (cid:80) i x n [ i ]  o k x n [ i ] . Leave One Out (loo). This measures the sensitivity of o k to the removal of a sentence. We refer to this measure as loo x n ,k = ( o k  o k,  n ) , where o k,  n is the new logit of relation k after removing sentence x n from its bag. We propose two different approaches for improving relation extraction. The first method we propose, introduces a subtle change to the representation of the sentences, which lead to higher performance and better explanation quality. We further propose to automatically generate distractor sentences and train the model to appropriately ignore them. Sentence representation. Each sentence in a bag contains entity mentions m i and m j for entities e i and e j respectively. In prior work m i and m j are kept unchanged (Lin et al., 2016; Beltagy et al., 2019). We argue that when entity mentions are used to compute the sentence representation, they provide such rich information that the model may not need to look at the rest of the sentence to deduce a relation. To ensure that our predictions are supported by appropriate sentences, we need to remove this effect. We propose to replace the entity mentions with their Fine-Grained Entity Types (FGET) Ling and Weld (2012) to force the model to identify the relations through the sentences. Learning from distractors. Prior work studied learning from human provided rationales (Lei et al., 2016; Ross et al., 2017; Bao et al., 2018; Ghaeini et al., 2019) in order to improve model explainability. However, human rationales are expensive to acquire. In this work we propose to learn from automatically generated distractor sentences. Let B i,j be a positive training bag (contains at least one relation) with entities ( e i , e j ) of FGET ( t i , t j ) . Let R ij ( | R ij | > 1) be the set of annotated relations for B i,j . For each k in R ij , we sample a distractor sentence s (cid:48) k from the set of sentences in the training set such that 1) it belongs to a bag whose FGET is ( t i , t j ) 2) the bag is not annotated with relation label k . If s (cid:48) k is not found this way, we simply choose a random sentence from a random negative bag (bag with no relation). Given s (cid:48) k , we replace its entity mentions with e i and e j (or t i and t j for FGET-based sentence representation) of a sentence in B i,j and add it to the bag, resulting in an augmented bag B (cid:48) i,j for relation k . To learn from the augmented bags, we feed B (cid:48) i,j into the model and the goal is to lower the contribution of the distractor sentence in relation to the original sentences in the bag. Specifically, we use GI to measure the sentence-level contribution and define the distractor loss for relation k as follows: l (cid:48) d,k = max (0 ,  + GI x (cid:48) k ,k  max x  B i,j GI x,k ) + | GI x (cid:48) k ,k | (7) where x (cid:48) k is the encoding of distractor sentence s (cid:48) k and  is a hyper-parameter for margin. The first term ensures that the contribution of the distractor is lower than the maximum contribution of all the sentences in the original bag and the second term reduces the absolute contribution of the distractor. Although we use GI in Eq.7, other explanation measures such as saliency or the positive portion of the contributions can also be applied here. Moreover a more advanced mechanism for generating distractors will likely lead to a higher performance. We hence update the loss in Eq. 6 with: l m = l + l (cid:48) d (8) where l (cid:48) d = (cid:80) k l (cid:48) d,k and  tradeoffs the regular learning loss with the distractor loss. Dataset. Similar to our baselines and prior work, we use the modified version of FB-NYT dataset. The original FB-NYT dataset was built by Riedel et al. (2010) on New York Times articles which was aligned to Freebase facts. It later was modified by Lin et al. (2016). There are 52 relations in this dataset where place lived, captial, neighbor-hood of, natinality and location are the most frequent relations. Tab. 1 shows the size of the modified dataset. Setup and Training. All models are implemented in PyTorch, trained with a Adam optimizer with learning rate 0.001 for a maximum of 30 epochs. We use 300-d skip-gram (Mikolov et al., 2013) word embeddings and FGET embeddings and 5-d position embedding. During training we freeze the word and entity embeddings. All reported results are averaged over three different random runs. We train on 90% of the training set and keep the remaining 10% for validation. We select  from the set { 0 . 01 , 0 . 1 , 1 . 0 , 10 . 0 , 100 . 0 } and set  = 1 . 0 based on validation AUC and the margin is fixed at  = 0 . 00001 . Ground-truth explanations. There are 1950 positive bags (6444 sentences) in the test split of FB-NYT. For each pair of sentence-relation in a bag we annotate whether the sentence entails the relation or not. Based on the annotations, we extract a set called expl-eval (see Appendix A.2 for details) including tuples of (bag-id, relation, positive sentence in bag, negative sentence in bag). Each tuple provides a desired ordering of two sentences when measuring their importance to the model. expl-eval is then used to compute the Kendall Tau correlation between the annotation and the explanations, which measures how consistently the importance weights ranks the sentences compared to the ground truth. Similar to prior work we use precision-recall (PR) curves to characterize the extraction performance and report the area under the PR curve (AUC) up to 0.4 recall. Tab. 2 reports the AUCs of the baselines and different variants of our proposed models with (+E) and without (-E) incorporating entity embeddings. Specifically, we consider two different ways of incorporating the FGET representations. Rows 3-4 show the AUCs of the two baseline models when we replace entity mentions with their FGET (+F), whereas rows 5-6 show the AUCs when we concatenate the FGET with the entity mentions (+FE). From the results we can see that both baselines see clear performance gain from incorporating FGET into the representations. Combining FGET with entity mention (+FE) achieves higher performance than using only FGET (+F), but our hypothesis is that the former will lead to less explainable models, which we will examine in the next section. Finally the last three rows of the table show that adding LD to different base models can further improve model loo (H) loo (L) S x n,k (H) S x n,k (L) GI x n,k (H) GI x n,k (L)  x n (H)  x n (L) CNNs+ATT 0.16 -0.08 0.19 -0.02 0.20 0.04 0.69 0.21 DirectSup 0.19 0.12 0.08 0.15 0.29 0.19 0.26 -0.12 CNNs+ATT +F 0.21 0.10 0.36 0.03 0.23 0.00 0.73 0.11 DirectSup +F 0.24 0.15 0.31 -0.19 0.40 -0.17 0.28 0.15 CNNs+ATT +FE 0.01 -0.11 0.21 -0.14 0.20 -0.20 0.24 0.01 DirectSup +FE 0.14 -.12 0.19 -0.10 0.29 0.06 0.17 -0.11 CNNs+ATT +LD 0.18 -0.01 0.22 0.10 0.21 0 0.67 0.11 CNNs+ATT +LD +F 0.22 -0.11 0.43 0.09 0.28 0.07 0.70 0.12 DirectSup +LD +F 0.23 0.14 0.38 0.01 0.49 0.20 0.45 0.02 H:Highconfidence P ( r )  [0 . 76 , 1 . 0] L:Lowconfidence P ( r )  [0 , 0 . 25] Table 3: Kendall correlations for top confidence and least confidence range. Similar to prior work, we observe that incorporating entity embeddings(+E) to the model leads to substantial performance gain across the board. We also observe very similar performance gain when adding FGET and LD to the base models both with and without entity embeddings. Our best model achieved an AUC of 0.341, which improves the previous state-of-the-art by 5.7%. We apply the explanation mechanisms described in Section 4 to produce sentence importance scores for the test set and compute the Kendall Tau correlations for the importance scores using expl-eval . For each model, to understand its behavior when it predicts correctly versus incorrectly, we consider the subset H ( L ) of bags/relations that the model outputs high (low) probability, i.e., p  [0 . 76 , 1] ( [0 , 0 . 25] ), for the correct relation. We report the performance on H and L separately in Tab. 3. Comparing correlation values for H and L in Tab. 3, we observe that when the models are making correct and confident predictions ( H ), the values of correlation tend to be higher. In contrast, when the model fails to detect the correct relation ( L ), we see substantially lower correlation scores. By replacing entity mentions with their FGET in both CNNs+ATT and DirectSup (+F), we observe substantially increased correlation scores for correct predictions (H). The improvement is consistent across all methods that are used to compute the importance scores. Recall that Tab. 2 shows that concatenating FGET with entity mention (+FE) yields improved relation extraction performance for both CNNs+ATT and DirectSup. In contrast, the explanation results presented here show that this comes at the cost of explainability, as demonstrated by the substantially lower correlation scores of CNNs+ATT+FE and DirectSup+FE. This confirms our conjecture that removing entity mentions from the sentence representation leads to more explainable models, possibly by forcing the model to focus on the textual evidence contained in the sentence rather than the word embedding of the mentions. Finally, we note that adding LD further improves the correlation score on H for S , GI and  . This suggests that learning from distractors is a valuable strategy that not only produces better relation extraction performance, but also enhances the model explanability. In this work we provided an annotated test set with ground-truth sentence-level explanations to evaluate the explanation quality of relation extraction models with distant supervision. Our examination of two baselines show that a model with lower relation extraction accuracy could have higher explanation quality. We proposed methods to improve both the accuracy and explainability. Our proposed methods are based on changing the representation of the sentences and learning from distractor to teach the model to ignore irrelevant information in a bag. Our evaluation on the widely used FB-NYT dataset show the effectiveness of our method in achieving state-of-the art performance in both accuracy and explanation quality.
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