Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder (2020)

Abstract: We propose an interpretable approach for event extraction that mitigates the tension between generalization and interpretability by jointly training for the two goals. Our approach uses an encoder-decoder architecture, which jointly trains a classifier for event extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the event classifier. We evaluate the proposed approach on three biomedical events and show that the decoder generates interpretable rules that serve as accurate explanations for the event classifier’s decisions, and, importantly, that the joint training generally improves the performance of the event classifier. Lastly, we show that our approach can be used for semi-supervised learning, and that its performance improves when trained on automatically-labeled data generated by a rule-based system.

## introduction interpretability is a key requirement for machine learning (ml) in many domains, e.g., legal, medical, finance. in the words of @xcite , "if users do not trust the model or a prediction, they will not use it." however, there is a tension between generalization and interpretability in deep learning, as interpretable models are often generated by "distilling" a model with good generalization, e.g., a deep learning one that relies on distributed representations, into models that are more interpretable but lose some generalization, e.g., linear models or decision trees @xcite @xcite . here, we argue that both generalization and interpretability are equally important. for example, in the medical space, a patient will likely reject a treatment recommended by an algorithm without an explanation. closer to natural language processing (nlp), a statistical information extraction method that converts free text in a specific domain to structured knowledge should also provide human-understandable explanations of its extractions. this allows the subject matter expert to quality check such output without a deep knowledge of the underlying machinery, which is a necessity in successful inter-disciplinary nlp collaborations. in this work, we propose an interpretable approach for event extraction (ee) that mitigates the tension between generalization and interpretability through multitask learning (mtl). our approach uses an attention-based encoder to encode the input text and given entities of interest (e.g., proteins in the biomedical domain), and a decoder that jointly trains two tasks. the first task is event classification, which identifies which event applies for a given entity (e.g., phosphorylation). the second task decodes a rule in the odin language @xcite , which explains the prediction of the classifier in a format that can be read and understood by human end users. an example of such a rule is shown in figure 1 . importantly, both tasks share the same encoder, and are trained using a joint objective function. supporting earlier findings, we observe that joint training leads to performance improvements both within and across tasks. in our unique pairing of tasks, however, we are able to shed light on an opaque process by generating rules that provide an interpretable distillation of an event classifier's decisions. the major contributions of this paper are: figure 1 : an example of an event extraction rule in the odin language that extracts phosphorylation events driven by a nominal trigger ("phosphorylation"). the event's sole argument or theme (the phosphorylated protein) is identified through both semantic constraints (its type must be protein), and syntactic ones (it must be attached to the trigger through a certain syntactic dependency pattern: a prep of followed by an optional (?) appositive (appos), followed by up to two ({,2}) other dependencies, e.g., nn). this rule would extract a phosphorylation(pkc) event from the text ". . . which includes the phosphorylation of pkc by. . . ". interpretability in machine learning is an area of active research involving a multitude of approaches. in this work, we focus on post-hoc interpretations that explain a model's output @xcite . a common theme of prior research in interpretable machine learning is producing a definite decision process (e.g., a decision tree) that preserves generalization. @xcite explored converting a trained network to a decision tree. similarly, @xcite trained soft binary decision trees using the predictions of a neural model. these decision trees are trained with mini-batch gradient descent using as labels a trained network's results. in the same vein, @xcite proposed a mimic learning framework, which trains gradient boosting trees to mimic the soft predictions of the original neural network. one unaddressed challenge with this direction, however, is that a decision tree's interpretability tends to decay as the tree increases in size. rather than converting a statistical model into an interpretable model such as a decision tree, other efforts have focused on jointly learning a statistical model with explanations for the model's output. our work falls in this camp as well. @xcite proposed a system for image classification that generates a natural language (nl) explanation to accompany each decision. similarly, @xcite learned nl explanations for the natural language inference (nli) task, and @xcite applied this idea to crime case prediction. inspired by such approaches, here we learn to generate declarative information extraction rules that serve to explain the predictions of an event classifier. ## approach our approach jointly addresses classification and interpretability through an encoder-decoder architecture, where the decoder uses mtl for event extraction (task 1) and rule generation (task 2). in this paper, we apply this architecture to the extraction of unary events in the biomedical domain. the two tasks are framed as follows: task 1 (t1): given a sentence and an entity in focus, it must identify which event applies to the entity, and what is its trigger, i.e., the verbal or nominal predicates that drives the lexicalization of the event (e.g., "phosphorylation"). task 2 (t2): decode a rule in the odin language that explains the prediction of the event classifier. that is, the rule should identify the lexical constraints on the event trigger, e.g., its lemma, the semantic type expected of the argument, e.g., that is must be a protein, and the syntactic pattern that connects the event trigger with the argument (figure 1 shows a complete example for such a rule). consider this text as a walkthrough example: which includes the phosphorylation of pkc by . . . , where the text in bold indicates the entity that is provided in the input in this task. this follows the settings of the standard event extraction task of bionlp 2013 @xcite . for task 1, we train a series of binary event classifiers (one for each event type), which predict the position of the event's lexical predicate (i.e., trigger) that modifies each given entity (phosphorylation here). drawing upon the state information from task 1, we prime our decoder in task 2 using a contextualized representation of the predicted event trigger to generate an information extraction rule in the odin language that captures the same event (i.e., entity-predicate structure) identified in task 1 (see figure 1 ). we detail these two tasks next. ## conclusions we introduced an interpretable approach for event extraction that jointly trains an event classifier with a component that translates the classifier's decisions into interpretable extraction rules. we implemented this approach using an encoder-decoder architecture, where the decoder jointly optimizes the decoding of extraction rules and event classification. we evaluated the proposed approach on three biomedical events and demonstrated that the decoder generates interpretable rules, and that the joint training improves the performance of the event classifier. we also showed that the performance of our approach further improves when trained on automatically-labeled data generated by a rule-based system. in the longer term, we envision a decoder with constraints, which enforces that the generated rules follow correct odin syntax. we plan to include constraints as part of decoding to aid in rule synthesis. for example, in the odin language, brackets must be paired to produce syntactically valid rules. this can be enforced with different strategies in the decoder, ranging from constrained greedy decoding to globally optimal solutions that could be implemented with integer linear programming. we suspect that including such validity constraints will further improve the quality of the decoded rules. further, we plan to use this decoder in an iterative, semi-supervised learning scenario akin to co-training @xcite . that is, the newly decoded, executable rules can be applied over large, unannotated texts to generate new training examples for the event classifier.