Weakly- and Semi-supervised Evidence Extraction (2020)

Abstract: For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness. However, in practice, evidence annotations may only be available for a minority of training examples (if available at all). In this paper, we propose new methods to combine few evidence annotations (strong semi-supervision) with abundant document-level labels (weak supervision) for the task of evidence extraction. Evaluating on two classification tasks that feature evidence annotations, we find that our methods outperform baselines adapted from the interpretability literature to our task. Our approach yields gains with as few as hundred evidence annotations.

## introduction despite the success of deep learning for countless prediction tasks, practitioners often desire that these models not only be accurate but also provide interpretations or explanations @xcite . unfortunately, these terms lack precise meaning, and across papers, such explanations purport to address a wide spectrum of desiderata, and it seems unlikely any one method could address them all @xcite . in both computer vision @xcite and natural language processing @xcite , proposed explanation methods often take the form of highlighting salient features of the input. these socalled local explanations are intended to highlight features that elucidate "the reasons behind predictions" @xcite . however, this characterization of the problem remains under-specified. in this paper, we instead focus on supplementing predictions with evidence, which we define as information that gives users the ability to quickly verify the correctness of predictions. fortunately, for many problems, a localized portion of the input is sufficient to validate the predicted label. in a large image, a small patch of an image containing a hamster may be sufficient to render the "hamster" label applicable. similarly, in a long clinical note, a single sentence may suffice to confirm a predicted diagnosis. this ability to verify results engenders trust among users, and increases adoption of the machine learning systems @xcite @xcite . in thus motivated, we cast our problem as learning to extract evidence using both strong and weak supervision. the former takes the form of explicit, but scarce, manual annotations of evidence segments, whereas the latter is provided by documents and their class labels which we assume are relatively abundant. 2 in the extreme case where evidence annotations are available for all examples, our task reduces to a standard multitask learning problem. in the opposite extreme, where only weak supervision is available, we find ourselves back in the under-specified realm addressed by local explanations. while predictive tokens may be extracted using only weak supervision, evidence extraction requires some amount of strong supervision. we draw inspiration from @xcite , who study the reverse problem-how to leverage marked evidence spans to improve classification performance. we optimize the joint likelihood of class labels and evidence spans, given the input examples. we factorize our objective such that we first classify, and then extract the ## related work we briefly discuss methods from the interpretability literature that aim to identify salient features of the input. @xcite propose an approach wherein a generator first extracts a subset of the text from the original input, which is further fed to an encoder that classifies the input by using only the extracted subset. the generator and encoder are trained end-to-end via reinforce-style optimization @xcite . however, follow-up work discovered the end-to-end training to be quite unstable with high variance in results @xcite . consequently, other approaches adopted the core idea of extract, and then classify in different forms: @xcite decouple the extraction and prediction modules and train them individually with intermediate supervision; @xcite use heuristics, like attention scores, for extraction; and lastly, paranjape et al. (2020) extract subsets that have high mutual information with the output variable and low mutual information with the input variable. ## extracting evidence formally, let the training data consist of n points {(x 1 , y 1 )...(x n , y n )}, where x i is a document, y i is the associated label. we assume that for m points (m n) we also have evidence annotations e i , a binary vector such that e @xmath0, and 0 otherwise. the conditional likelihood of the output labels and evidence given the documents can be written as: we can factorize this likelihood in two ways. first, this corresponds to the extract, then classify approach. since both components of this likelihood function require extractions, supervised methods can only leverage m (out of n) training examples @xcite . unsupervised or semisupervised extraction methods can still use all the document-level labels during training @xcite . alternatively, we can factorize the likelihood as follows: the classify, then extract approach is amenable to weakly supervised learning, as we can optimize the classification objective for all n examples, and movie review i don't know what movie the critics saw, but it wasn't this one. the popular consensus among newspaper critics was that this movie is unfunny and dreadfully boring . in my personal opinion, they couldn't be more wrong. if you were expecting airplane! -like laughs and agatha christie -intense mystery, then yes, this movie would be a disappointment. however, if you're just looking for an enjoyable movie and a good time , this is one to see ... lean, mean, escapist thrillers are a tough product to come by. most are unnecessarily complicated , and others have no sense of expediency-the thrill-ride effect gets lost in the cumbersome plot. perhaps the ultimate escapist thriller was the fugitive, which featured none of the flash-bang effects of today's market but rather a bread-and-butter, textbook example of what a clever script and good direction is all about. ... the extraction objective for m examples. we use bert @xcite to model p θ (y|x), and a linear-chain crf @xcite to model p φ (e|x, y; θ), where: here t indexes the input sequence, and z is a normalization factor. function f (•) extracts k features including both emission and transition features, and φ are the corresponding weights. the transition weights allow the crf to model contiguity in the evidence tokens. we examine two types of emission features for a given token x t in the input x, including (1) bert features (f bert (x) t ) where we encode the entire input sequence, and use the representation corresponding to token x t ; foot_2 and (2) attention features where we use the last layer attention values from different heads of the [cls] token to the given token x t . these features tie the classification and extraction architectures. the classify, then extract approach also allows conditioning the evidence extraction model on the (predicted or oracle) label of the text document. for binary classification, one way to achieve this is to transform the existing emission features f to new features f in the following manner: this transformation allows us to use even indexed emission weights (φ 2k ) for the first class, and odd indexed emission weights (φ 2k+1 ) for the second class. similar transformations can be easily constructed for multi-class classification problems. during inference we use the predicted label ŷ instead of the true label y. using this formulation, emission features (and their corresponding weights) capture the association of each word with the extraction label (evidence or not) and the classification label. for instance, for binary sentiment analysis of movie reviews, the token "brilliant" is highly associated with the positive class, and if the review is (marked/predicted to be) positive, then the chances to select it as a part of the evidence increase. inversely, if "brilliant" occurs in a negative review, the chances of selecting it decrease. by conditioning the extraction models on the classification label, one can find supporting evidence tailored for each class (as one can see in implementation details we train both the classification and extraction modules simultaneously. for evidence extraction, the emission features of the crf include bert representations or its attention values (depending upon the experiment). the same bert model is also used for classification, thus the two tasks share the bert parameters. we use the transformers library by hugging face @xcite , and default optimization parameters for finetuning bert. ## conclusion we present a simple technique to supplement predictions with evidence by jointly modeling the text classification and evidence sequence labeling tasks. we show that conditioning the evidence extraction on the predicted label, in a classify then extract framework, leads to improved performance over baselines with as few as a hundred annotations. it also allows generating evidence for each label, which can enable stakeholders to better verify the correctness of predictions.