SlowGuess/ACL_2020_OCR huggingface dataset:
We directly adopt a publicly available medical co-occurrence graph for our experiments (Finlayson et al., 2014). The graph was constructed in the following way: Finlayson et al. (2014) first used an efficient annotation tool (LePendu et al., 2012) to extract medical terms from 20 million clinical notes collected by Stanford Hospitals and Clinics, and then computed the co-occurrence counts of two terms based on their appearances in one patient's records within a certain time frame (e.g., 1 day, 7 days). We experiment with their biggest dataset with the largest number of nodes (i.e., the per-bin 1-day graph here<sup>7</sup>) so as to have sufficient training data. The co-occurrence graph contains 52,804 nodes and 16,197,319 edges.
To obtain training labels for relation prediction, we utilize the mapping between medical terms and concepts provided by Finlayson et al. (2014). To be specific, they mapped extracted terms to UMLS concepts with a high mapping accuracy by suppressing the least possible meanings of each term (see Finlayson et al. (2014) for more details). We utilize such mappings to automatically collect relation labels from UMLS. For term  $e_{a}$  and  $e_{b}$  that are respectively mapped to medical concept  $c_{A}$  and  $c_{B}$ , we find the relation between  $c_{A}$  and  $c_{B}$  in UMLS, which will be used as the label for  $e_{a}$  and  $e_{b}$ .
Following Wang and Fan (2014) that studied distant supervision in medical text and identified several crucial relations for clinical decision making, we select 5 important medical relations with no less than 1,000 relation triples in our dataset. Each relation is mapped to UMLS semantic relations, e.g., relation CAUSES corresponds to cause_of, induces, causative_agent_of in UMLS. A full list of mapping is in the appendix. We sample an equal number of negative pairs by randomly pairing head and tail entities with the correct argument types (Wang
Med Relations	Train	Dev	Test
Symptom of	14,326	3,001	3,087
May treat	12,924	2,664	2,735
Contraindicates	10,593	2,237	2,197
May prevent	2,113	440	460
Causes	1,389	305	354
Total	41.3k	8.6k	8.8k
Table 1: Dataset Statistics.
et al., 2016). We split all samples into train/dev/test sets with a ratio of 70/15/15. Only relation triples in the training set are used to optimize relational parameters. The statistics of the positive samples for relations are summarized in Table 1.
4.2 Predictive Performance Evaluation
Compared Methods. There are a number of advanced neural methods (Tang et al., 2015; Qu et al., 2018; Wang et al., 2018) that have been developed for the link prediction task, i.e., predicting the relation between two nodes in a co-occurrence graph. At the high level, their frameworks comprise of an entity encoder and a relation scoring function. We adapt various existing methods for both the encoder and the scoring functions for comprehensive comparison. Specifically, given the co-occurrence graph, we employ existing distributional representation learning methods to learn entity embeddings. With the entity embeddings as input features, we adapt various models from the knowledge base completion literature as a binary relation classifier. More specifically, for the encoder, we select one word embedding method, Word2vec (Mikolov et al., 2013; Levy and Goldberg, 2014), two graph embedding methods, random-walk based DeepWalk (Perozzi et al., 2014), edge-sampling based LINE (Tang et al., 2015), and one distributional approach REPEL-D (Qu et al., 2018) for weakly-supervised relation extraction that leverages both the co-occurrence graph and training relation triples to learn entity representations. For the scoring functions, we choose DistMult (Yang et al., 2015), RESCAL (Nickel et al., 2011) and NTN (Socher et al., 2013).
Note that one can apply more complex encoders or scoring functions to obtain higher predictive performance; however, in this work, we emphasize more on model interpretability than predictive performance, and unfortunately, all such frameworks are hard to interpret as they provide little or no
$^{7}$ https://datadryad.org/stash/dataset/doi:10.5061/dryad.jp917
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Methods	MAY_TREAT	CONTRAIN.	SYMPTOM_OF	MAY_PREVENT	CAUSES	Avg.
Word2vec + DistMult	0.767 (±0.008)	0.777 (±0.013)	0.815 (±0.005)	0.649 (±0.018)	0.671 (±0.015)	0.736
Word2vec + RESCAL	0.743 (±0.010)	0.767 (±0.003)	0.808 (±0.009)	0.658 (±0.023)	0.659 (±0.039)	0.727
Word2vec + NTN	0.693 (±0.013)	0.758 (±0.005)	0.808 (±0.004)	0.605 (±0.022)	0.631 (±0.017)	0.699
DeepWalk + DistMult	0.740 (±0.003)	0.776 (±0.004)	0.805 (±0.003)	0.608 (±0.014)	0.650 (±0.018)	0.716
DeepWalk + RESCAL	0.671 (±0.010)	0.778 (±0.003)	0.800 (±0.003)	0.600 (±0.023)	0.708 (±0.011)	0.711
DeepWalk + NTN	0.696 (±0.006)	0.778 (±0.005)	0.787 (±0.005)	0.614 (±0.016)	0.674 (±0.024)	0.710
LINE + DistMult	0.767 (±0.003)	0.783 (±0.002)	0.795 (±0.003)	0.621 (±0.015)	0.641 (±0.024)	0.721
LINE + RESCAL	0.725 (±0.003)	0.771 (±0.002)	0.801 (±0.001)	0.613 (±0.013)	0.694 (±0.015)	0.721
LINE + NTN	0.733 (±0.002)	0.773 (±0.003)	0.800 (±0.001)	0.601 (±0.015)	0.706 (±0.013)	0.723
REPEL-D + DistMult	0.784 (±0.002)	0.797 (±0.002)	0.809 (±0.003)	0.681 (±0.010)	0.694 (±0.022)	0.751
REPEL-D + RESCAL	0.726 (±0.003)	0.780 (±0.002)	0.776 (±0.002)	0.685 (±0.010)	0.708 (±0.003)	0.737
REPEL-D + NTN	0.736 (±0.004)	0.780 (±0.002)	0.773 (±0.001)	0.667 (±0.015)	0.694 (±0.024)	0.731
Ours (w/ CWA)	0.709 (±0.005)	0.751 (±0.009)	0.744 (±0.007)	0.667 (±0.008)	0.661 (±0.032)	0.706
Ours	0.805 (±0.017)	0.811 (±0.006)	0.816 (±0.004)	0.676 (±0.020)	0.684 (±0.017)	0.758
explanations on how predictions are made.
We also show the predictive performance of our framework under the CWA setting in which the CWA rationales are existing triples in a "closed" knowledge base (i.e., UMLS). We first adopt the pre-trained association recall module to retrieve associative contexts for head and tail entities, then formulate the assumptions using top-ranked triples (that exist in our relation training data), where the rank is based on the product of their retrieval probabilities  $(p_{ij} = p(e_i|e_h)\times p(e_j|e_t))$ . We keep the rest of our model the same as the OWA setting.
Results. We compare the predictive performance of different models in terms of F1 score under each relation prediction task. As shown in Table 2, our model obtains very competitive performance compared with a comprehensive list of baseline methods. Specifically, on the prediction tasks of MAY_TREAT and CONTRAINDICATES, our model achieves a substantial improvement (1~2 F1 score) and a very competitive performance on the task of SYMPTOM_OF and MAY_PREVENT. The small amount of training data might partly explain why our model does not perform so well in the CAUSES tasks. Such comparison shows the effectiveness of predicting relations based on associations and their relational interactions. Moreover, compared with those baseline models which encode graph structure into latent vector representation, our model utilizes co-occurrence graph more explicitly by leveraging the associative contexts symbolically to generate human-understandable rationales, which can assist medical experts as we will see shortly. In addition, we observe that our model consistently
Table 2: Comparison of model predictive performance. We run all methods for five times and report the averaged F1 scores with standard deviations.
OWA Rationales	CWA Rationales
Ranking Score	17	5
Avg. Sum Score/Case	6.14	2.24
Avg. Max Score/Case	2.04	0.77
Table 3: Human evaluation on the quality of rationales.
outperforms the CWA setting: Despite the CWA rationales are true statements on their own, they tend to have a low coverage of possible rationales, and thus, may be not so relevant for the target relation prediction, which leads to a poor predictive performance.
4.3 Model Rationale Evaluation
To measure the quality of our model rationales (i.e., OWA rationales), as well as to conduct an ablation study of our model, we conduct an expert evaluation for the OWA rationales and also compare them with the CWA rationales. We first collaborate with a physician to explore how much a model's rationales help them better trust the model's prediction following recent work for evaluating model interpretability (Singh et al., 2019; Mullenbach et al., 2018; Atutxa et al., 2019; Jin et al., 2020). Then, we present some case studies to show what kind of rationales our model has learnt. Note that compared with evaluation by human annotators for open-domain tasks (without expertise requirement), evaluation by medical experts is more challenging in general. The physician in our study (an M.D. with 9 years of clinical experience and currently a fellow trained in clinical informatics), who is able to understand the context of terms and the basics of the compared algorithms and can dedicate time, is qualified for our evaluation.
Expert Evaluation. We first explained to the physician about the recall and recognition process in our framework and how model rationales are developed. They endorsed such reasoning process as one possible way to gain their trust in the model. Next, for each target pair for which our model correctly makes the prediction, they were shown the top-5 rationales produced by our framework and were asked whether each rationale helps them better trust the model prediction. For each rationale, they were asked to score it from 0 to 3 in which 0 is no helpful, 1 is a little helpful, 2 is helpful and 3 is very helpful. In addition to the individual rationale evaluation, we further compare the overall quality of CWA and OWA rationales, by letting experts rank them based the helpfulness of each set of rationales (the rationale set ranked higher gets 1 ranking score and both get 0 if they have the same rank). We refer readers to the appendix for more details of the evaluation protocol. We randomly select 30 cases in the MAY_TREAT relation and the overall evaluation results are summarized in Table 3. Out of 30, OWA wins in 17 cases and gets higher scores on individual rationales per case on average. There are 8 cases where the two sets of rationales are ranked the same<sup>8</sup> and 5 cases where CWA is better. To get a better idea of how the OWA model obtains more trust, we calculate the average sum score per case, which shows the OWA model gets a higher overall score per case. Considering in some cases only a few rationales are able to get non-zero scores, we also calculate the average max score per case, which shows that our OWA model generally provides one helpful rationale (score  $>2$ ) per case. Overall, as we can see, the OWA rationales are more helpful to gain expert trust.
Case Study. Table 4 shows two concrete examples demonstrating what kind of model rationales our framework bases its predictions on. We highlight the rationales that receive high scores from the physician for being especially useful for trusting the prediction. As we can see, our framework is able to make correct predictions based on reasonable rationales. For instance, to predict that "cephalosporine" may treat "bacterial infection", our model relies on the rationale that "cefuroxime" may treat "infectious diseases". We also note that not all rationales are clinically established facts or even make sense, due to the unsupervised rationale learning and the probabilistic assumption formation
Case 1
cephalosporins	may_treat	bacterial infection
cefuroxime	may_treat	viral syndrome
cefuroxime	may_treat	low grade fever
cefuroxime	may_treat	infectious diseases
cefuroxime	may_prevent	low grade fever
sulbactam	may_treat	low grade fever
Case 2
azelastine	may_treat	perennial allergic rhinitis
astpro	may_treat	perennial allergic rhinitis
pseudoephedrine	may_treat	perennial allergic rhinitis
ciclesonide	may_treat	perennial allergic rhinitis
overbite	may_treat	perennial allergic rhinitis
diclofenac	may_treat	perennial allergic rhinitis
Table 4: Case studies for rationalizing medical relation prediction. For each case, the first panel is target pair and the second is top-5 rationales (Bold ones are useful rationales with high scores from the physician). The left (right) most column is the head (tail) term and their relational associations.
process, which leaves space for future work to further improve the quality of rationales. Nevertheless, such model rationales can provide valuable information or new insights for clinicians. For another example, as pointed out by the physician, different medications possibly having the same treatment response, as shown in Case 2, could be clinically useful. That is, if three medications are predicted to possibly treat the same condition and a physician is only aware of two doing so, one might get insights into trying the third one. To summarize, our model is able to provide reasonable rationales and help users understand how model predictions are made in general.
Relation Extraction (RE) typically focuses on predicting relations between two entities based on their text mentions, and has been well studied in both open domain (Mintz et al., 2009; Zeng et al., 2015; Riedel et al., 2013; Lin et al., 2016; Song et al., 2019; Deng and Sun, 2019) and biomedical domain (Uzuner et al., 2011; Wang and Fan, 2014; Sahu et al., 2016; Lv et al., 2016; He et al., 2019). Among them, most state-of-the-art work develops various powerful neural models by leveraging human annotations, linguistic patterns, distance supervision, etc. More recently, an increasing amount of work has been proposed to improve model's transparency and interpretability. For example, Lee et al. (2019) visualizes self-attention weights learned from BERT (Devlin et al., 2019) to explain relation prediction. However, such text-based interpretable
<sup>8</sup>Of which, 7 cases are indicated equally unhelpful.
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models tend to provide explanations within a local context (e.g., words in a single sentence mentioning target entities), which may not capture a holistic view of all entities and their relations stored in a text corpus. We believe that such a holistic view is important for interpreting relations and can be provided to some degree by the global statistics from a text corpus. Moreover, global statistics have been widely used in the clinical domain as they can better preserve patient privacy (Finlayson et al., 2014; Wang et al., 2019).
On the other hand, in recent years, graph embedding techniques (Perozzi et al., 2014; Tang et al., 2015; Grover and Leskovec, 2016; Yue et al., 2019) have been widely applied to learn node representations based on graph structure. Representation learning based on global statistics from a text corpus (i.e., co-occurrence graph) has also been studied (Levy and Goldberg, 2014; Pennington et al., 2014). After employing such methods to learn entity embeddings, a number of relation classifiers (Nickel et al., 2011; Bordes et al., 2013; Socher et al., 2013; Yang et al., 2015; Wang et al., 2018) can be adopted for relation prediction. We compare our method with such frameworks to show its competitive predictive accuracy. However, such frameworks tend to be difficult to interpret as they provide little or no explanations on how decisions are made. In this paper, we focus more on model interpretability than predictive accuracy, and draw inspirations from existing cognitive theories of recall and recognition to develop a new framework, which is our core contribution.
Another line of research related to interpreting relation prediction is path-based knowledge graph (KG) reasoning (Gardner et al., 2014; Neelakantan et al., 2015; Guu et al., 2015; Xiong et al., 2017; Stadelmaier and Padó, 2019). In particular, existing paths mined from millions of relational links in knowledge graphs can be used to provide justifications for relation predictions. For example, to explain Microsoft and USA may hold the relation CountryOfHeadquarters, by traversing a KG, one can extract the path Microsoft  $\xrightarrow{\text{IsBasedIn}}$  Seattle  $\xrightarrow{\text{CountryLocatedIn}}$  USA as one explanation. However, such path-finding methods typically require large-scale relational links to infer path patterns, and cannot be applied to our co-occurrence graph as the co-occurrence links are unlabeled.
In addition, our work is closely related to the area of rationalizing machine decision by generat
ing justifications/rationales accounting for model's prediction. In some scenarios, human rationales are provided as extra supervision for more explainable models (Zaidan et al., 2007; Bao et al., 2018). However, due to the high cost of manual annotation, model rationales are desired to be learned in an unsupervised manner(Lei et al., 2016; Bouchacourt and Denoyer, 2019; Zhao et al., 2019). For example, Lei et al. (2016) select a subset of words as rationales and Bouchacourt and Denoyer (2019) provide an explanation based on the absence or presence of "concepts", where the selected words and "concepts" are learned unsupervisedly. Different from text-based tasks, in this paper, we propose to rationalize relation prediction based on global cooccurrence statistics and similarly, model rationales in our work are captured without explicit manual annotation either, via a joint training framework.
In this paper, we propose an interpretable framework to rationalize medical relation prediction based on corpus-level statistics. Our framework is inspired by existing cognitive theories on human memory recall and recognition, and can be easily understood by users as well as provide reasonable explanations to justify its prediction. Essentially, it leverages corpus-level statistics to recall associative contexts and recognizes their relational connections as model rationales. Compared with a comprehensive list of baseline models, our model obtains competitive predictive performances. Moreover, we demonstrate its interpretability via expert evaluation and case studies.
We thank Srinivasan Parthasarathy, Ping Zhang, Samuel Yang and Kaushik Mani for valuable discussions. We also thank the anonymous reviewers for their hard work and constructive feedback. This research was sponsored in part by the Patient-Centered Outcomes Research Institute Funding ME-2017C1-6413, the Army Research Office under cooperative agreements W911NF-17-1-0412, NSF Grant IIS1815674, and Ohio Supercomputer Center (Center, 1987). The views and conclusions contained herein are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S.Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notice herein.
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