An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction (2020)

Abstract: Decisions of complex models for language understanding can be explained by limiting the inputs they are provided to a relevant subsequence of the original text — a rationale. Models that condition predictions on a concise rationale, while being more interpretable, tend to be less accurate than models that are able to use the entire context. In this paper, we show that it is possible to better manage the trade-off between concise explanations and high task accuracy by optimizing a bound on the Information Bottleneck (IB) objective. Our approach jointly learns an explainer that predicts sparse binary masks over input sentences without explicit supervision, and an end-task predictor that considers only the residual sentences. Using IB, we derive a learning objective that allows direct control of mask sparsity levels through a tunable sparse prior. Experiments on the ERASER benchmark demonstrate significant gains over previous work for both task performance and agreement with human rationales. Furthermore, we find that in the semi-supervised setting, a modest amount of gold rationales (25% of training examples with gold masks) can close the performance gap with a model that uses the full input.

## introduction a rationale is a short yet sufficient part of the input text that can explain model decisions for a range of language understanding tasks @xcite . models can be faithful to a rationale by only using the selected text as input for end-task prediction @xcite . however, there is almost always a trade-off between interpretable models that learn to extract sparse rationales and more accurate models that are able to use the full 1 our code is available at https://github.com/ bhargaviparanjape/explainable_qa ## model to optimize for objective 3, the posterior distribution estimator p θ () and label likelihood estimator q φ () are instantiated as the explainer and end-task predictor neural models respectively. two different pre-trained transformers @xcite are used to initialize both models. ## related work extractive rationalization methods that condition predictions on their explanations are more trustworthy than post-hoc explanation techniques @xcite @xcite and analyses of selfattention @xcite . extractive rationalization @xcite is one of the most well-studied of such methods and has received increased attention with the recently released eraser benchmark @xcite . chang et al. (2019) and yu et al. (2019); chang et al. (2019) have complementary work on class-wise explanation extraction. bastings et al. (2019) employ a reparameterizable version of the bi-modal beta distribution (instead of bernoulli) for the binary mask. while our method has focused on unsupervised settings due to the considerable cost of obtaining reliable rationale annotations, recent work (lehman et al., 2019) has also attempted to use direct supervision from rationale annotations for critical medical domain tasks. finally, latcinnik and berant (2020) and rajani et al. (2019) focus on generating explanations (rather than extracting them from the input). the extractive paradigm can be unfavourable for certain eraser tasks like commonsense question answering, where the given input provides limited context for the task. information bottleneck information bottleneck (ib) @xcite has recently been adapted in a number of downstream applications like parsing (li and eisner, 2019), extractive summarization @xcite , and image classification @xcite . alemi et al. (2016) and li and eisner (2019) use ib for optimal compression of hidden representations of images and words respectively. we are interested in compressing the number of cognitive units (like sentences) to ensure interpretability of the bottleneck representation, similar to west et al. (2019). however, while west et al. (2019) use brute-force search to optimize ib for summarization, we directly optimize a parametric variational bound on ib for rationales. ib has also been previously used for interpretability-zhmoginov et al. (2019) use a vae to estimate the prior distribution over z for image classification. bang et al. ( 2019 ) use ib for post-hoc explanation of sentiment classification. they do not enforce a sparse prior, and as a result, cannot guarantee that the rationale is strictly smaller than the input. controlling sparsity to manage the accuracy-conciseness trade-off is also not possible in their model. ## conclusion we introduce a novel sparsity-inducing objective derived from the information bottleneck principle to extract rationales of desired conciseness. our approach outperforms existing norm-minimization techniques in task performance and agreement with human rationales for tasks in the eraser benchmark. our objective obtains a better trade off of accuracy vs. sparsity. we are also able to close the gap with models that use the full input @xmath0. in future work, we would like to apply our approach on document-level and multi-document nlu tasks. by a limiting argument, we can compute the divergence between p θ (z|x) and r(z): the term kl[p θ (m j |x), r(m j )] is a divergence between two bernoulli distributions and has a simple closed form. if θ j (x) and log p(x) are uncorrelated then the term πh(x) is constant with respect to the parameters θ and can be dropped. we use the same, standard cross-entropy bound discussed in appendix a.1 to estimate i(z, y ), leading us to our variational bound on ib with interpretability constraints