Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decomposition (2022)

Abstract: Recent efforts to create challenge benchmarks that test the abilities of natural language understanding models have largely depended on human annotations. In this work, we introduce the “Break, Perturb, Build” (BPB) framework for automatic reasoning-oriented perturbation of question-answer pairs. BPB represents a question by decomposing it into the reasoning steps that are required to answer it, symbolically perturbs the decomposition, and then generates new question-answer pairs. We demonstrate the effectiveness of BPB by creating evaluation sets for three reading comprehension (RC) benchmarks, generating thousands of high-quality examples without human intervention. We evaluate a range of RC models on our evaluation sets, which reveals large performance gaps on generated examples compared to the original data. Moreover, symbolic perturbations enable fine-grained analysis of the strengths and limitations of models. Last, augmenting the training data with examples generated by BPB helps close the performance gaps, without any drop on the original data distribution.

## introduction evaluating natural language understanding (nlu) systems has become a fickle enterprise. while models outperform humans on standard benchmarks, they perform poorly on a multitude of distribution shifts @xcite @xcite . to expose such gaps, recent work has proposed to evaluate models on contrast sets @xcite , or counterfactually-augmented data @xcite , where minimal but meaningful perturbations are applied to test examples. however, since such examples are manually written, collecting them is expensive, and procuring diverse perturbations is challenging @xcite . recently, methods for automatic generation of contrast sets were proposed. however, current methods are restricted to shallow surface perturbations @xcite , specific reasoning skills @xcite , or rely on expensive annotations @xcite . thus, automatic generation of examples that test high-level reasoning abilities of models and their robustness to fine semantic distinctions remains an open challenge. in this work, we propose the ''break, perturb, build'' (bpb) framework for automatic generation of reasoning-focused contrast sets for reading comprehension (rc). changing the high-level semantics of questions and generating questionanswer pairs automatically is challenging. first, it requires extracting the reasoning path expressed in a question, in order to manipulate it. second, it requires the ability to generate grammatical and coherent questions. in figure 1 , for example, transforming q, which involves number comparison, into q1, which requires subtraction, leads to dramatic changes in surface form. third, it requires an automatic method for computing the answer to the perturbed question. our insight is that perturbing question semantics is possible when modifications are applied to a structured meaning representation, rather than to the question itself. specifically, we represent questions with qdmr @xcite , a representation that decomposes a question into a sequence of reasoning steps, which are written in natural language and are easy to manipulate. relying on a structured representation lets us develop a pipeline for perturbing the reasoning path expressed in rc examples. our method (see figure 1 ) has four steps. we (1) parse the question into its qdmr decomposition, (2) apply rule-based perturbations to the decomposition, (3) generate new questions from 1 ) parsing the question into its qdmr decomposition, (2) applying semantic perturbations to the decomposition, (3) generating a question for each transformed decomposition, and (4) computing answers/constraints to the new questions. the perturbed decompositions, and (4) compute their answers. in cases where computing the answer is impossible, we compute constraints on the answer, which are also useful for evaluation. for example, for q4 in figure 1 , even if we cannot extract the years of the described events, we know the answer type of the question (boolean). notably, aside from answer generation, all steps depend on the question only, and can be applied to other modalities, such as visual or table question answering (qa). running bpb on the three rc datasets, drop @xcite , hotpotqa @xcite , and iirc @xcite , yields thousands of semantically rich examples, covering a majority of the original examples (63.5%, 70.2%, and 45.1%, respectively). moreover, we validate examples using crowdworkers and find @xmath0. we demonstrate the utility of bpb for comprehensive and fine-grained evaluation of multiple rc models. first, we show that leading models, such as unifiedqa @xcite and tase @xcite , struggle on the generated contrast sets with a decrease of 13-36 f 1 points and low consistency (<40). moreover, analyzing model performance per perturbation type and constraints, reveals the strengths and weaknesses of models on various reasoning types. for instance, (a) models with specialized architectures are more brittle compared to general-purpose models trained on multiple datasets, (b) tase fails to answer intermediate reasoning steps on drop, (c) unifiedqa fails completely on questions requiring numerical computations, and (d) models tend to do better when the numerical value of an answer is small. last, data augmentation with examples generated by bpb closes part of the performance gap, without any decrease on the original datasets. in summary, we introduce a novel framework for automatic perturbation of complex reasoning questions, and demonstrate its efficacy for generating contrast sets and evaluating models. we expect that imminent improvements in question generation, rc, and qdmr models will further widen the accuracy and applicability of our approach. the generated evaluation sets and codebase are publicly available at https://github.com/mega002/qdmr -based-question-generation . ## background our goal, given a natural language question q, is to automatically alter its semantics, generating perturbed questions q for evaluating rc models. this section provides background on the qdmr representation and the notion of contrast sets. ## bpb: automatically generating semantic question perturbations we now describe the bpb framework. given an input @xmath1, c of question and context, and the answer a to q given c, we automatically map it to a set of new examples c(x) (figure 1 ). our approach uses models for question decomposition, question generation (qg), and rc. ## generated evaluation sets we run bpb on the rc datasets drop @xcite , hotpotqa @xcite , and iirc @xcite . questions from the training sets of drop and hotpotqa are included in break, and were used to train the decomposition and qg models. results on iirc show bpb's generalization to datasets for which we did not observe q, d pairs. statistics on the generated contrast and constraint sets are in ## experimental setting we use the generated contrast and constraints sets to evaluate the performance of strong rc models. ## related work the evaluation crisis in nlu has led to wide interest in challenge sets that evaluate the robustness of models to input perturbations. however, most past approaches @xcite @xcite involve a human-in-the-loop and are thus costly. recently, more and more work has considered using meaning representations of language to automatically generate evaluation sets. past work used an erg grammar @xcite and amr @xcite to generate relatively shallow perturbations. in parallel to this work, @xcite used control codes over srl to generate more semantic perturbations to declarative sentences. we generate perturbations at the level of the underlying reasoning process, in the context of qa. last, @xcite used scene graphs to generate examples for visual qa. however, they assumed the existence of gold scene graph at the input. overall, this body of work represents an exciting new research program, where structured representations are leveraged to test and improve the blind spots of pre-trained language models. more broadly, interest in automatic creation of evaluation sets that test out-of-distribution generalization has skyrocketed, whether using heuristics @xcite , data splits @xcite , adversarial methods @xcite , or an aggregation of the above @xcite . last, qdmr-to-question generation is broadly related to work on text generation from structured data @xcite @xcite , and to passage-to-question generation methods @xcite @xcite that, in contrast to our work, focused on simple questions not requiring reasoning. ## discussion we propose the bpb framework for generating high-quality reasoning-focused question perturbations, and demonstrate its utility for constructing contrast sets and evaluating rc models. while we focus on rc, our method for perturbing questions is independent of the context modality. thus, porting our approach to other modalities only requires a method for computing the answer to perturbed questions. moreover, bpb provides a general-purpose mechanism for question generation, which can be used outside qa as well. we provide a library of perturbations that is a function of the current abilities of rc models. as future rc models, qdmr parsers, and qg models improve, we can expand this library to support additional semantic phenomena. last, we showed that constraint sets are useful for evaluation. future work can use constraints as a supervision signal, similar to @xcite , who leveraged dependencies between training examples to enhance model performance. limitations bpb represents questions with qdmr, which is geared towards representing complex factoid questions that involve multiple reasoning steps. thus, bpb cannot be used when questions involve a single step, for example, one cannot use bpb to perturb ''where was barack obama born?''. inherently, the effectiveness of our pipeline approach depends on the performance of its modules-the qdmr parser, the qg model, and the single-hop rc model used for qdmr evaluation. however, our results suggest that current models already yield high-quality examples, and model performance is expected to improve over time.