Introspective Distillation for Robust Question Answering

1 Introduction UpDn [ 4 ] S-MRL [ 6 ] CFVQA+IntroD CSS+IntroD LMH+IntroD Ensemble-based Methods Ours LMH [ 11 ] CFVQA [ 25 ] CSS [ 7 ] Baseline Debiasing Methods Ours OOD Accuracy ( VQA-CP v2 test ) ID Ac cu ra cy ( V Q A v2 va l ) Figure 1 : Recent debiasing methods achieve high OOD accuracy with the sacrifice of ID accuracy . Our proposed IntroD makes the best of both worlds . Question answering ( QA ) , which requires machines to answer questions given a context , is one of the most fundamental AI tasks . Popular contexts are vision ( e.g. , image for VQA [ 5 ] ) and natural language ( e.g. , passage for extractive QA [ 27 ] ) . A common observation is that QA models prefer to over-exploit the training bias , which bypasses the context comprehension for a shortcut answer . For example , by only using the linguistic correlations between questions and answers , VQA models can answer most questions correctly [ 16 , 2 , 5 , 20 ] . Similarly , extractive QA models may use the spurious positional cues to locate the answer in the passage [ 22 ] . As a result , QA models that have already achieved strong in-distribution ( ID ) performance may inevitably fail in out-of-distribution ( OOD ) test scenarios , regardless of the scale of training data and models [ 14 , 22 , 37 ] . Recently , several debiasing methods aim to close the gap between the ID and OOD performances [ 6 , 11 , 7 , 25 ] . However , many of them hold the assumption that the training and test distributions are very different or even reversed , e.g. , if there are more “ yes ” answers in training , there must be more “ no ” answers in testing . As a result , these methods encounter a severe performance drop under the ID evaluation , although they significantly outperform non-debiasing baselines in terms of OOD performance . An interesting observation from Figure 1 is that non-debiasing methods ( circles ) obtain high ID but low OOD performance , while debiasing methods ( squares ) achieve high OOD but low ID performance . This observation motivates us to ask : can we make the best of both worlds ? 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . Question type Answer Distribution Question type Answer Distribution Question type Answer Distribution In this paper , we take a step forward to building robust QA models that achieve strong performances in both ID and ODD evaluations . We point out that if the model is over-exploiting the bias in one world , the performance in the other one would be significantly degraded . Therefore , the “ best of both ” model should be fair with the inductive bias in either world . To this end , we present a simple yet effective training paradigm—Introspective Distillation ( IntroD ) —to blend the inductive bias of both worlds fairly . Suppose that we have two expert teacher models : ID-teacher and OOD-teacher , each of which captures the ID or OOD inductive bias and represents the corresponding world . Figure 2 illustrates three cases about how an introspective student learns from the two very different teachers . Case 1 : if ID-bias > OOD-bias , then ID-teacher < OOD-teacher . ID inductive bias dominates the learning , and the student should listen more to OOD-teacher . This case occurs when ID-teacher has a low training loss while OOD-teacher has a high one . As shown in Figure 2 ( a ) , it is hard for QA models to conclude whether the oven is electric or not without additional context . Due to the inductive bias in the training data , i.e. , most questions starting with “ is ” are answered by “ yes ” , ID-teacher concludes with over-confidence while OOD-teacher does not . Case 2 : if ID-bias < OOD-bias , then ID-teacher > OOD-teacher . OOD inductive bias dominates the learning , and the student should listen more to ID-teacher . This case occurs when ID-teacher has a high training loss while OOD-teacher has a low one . As shown in Figure 2 ( c ) , there are at least two older men , one in a blue shirt selling fruits and one in a white shirt walking in the crowd . Therefore , both “ blue ” and “ white ” should be correct . However , as most training questions starting with “ what color ” are labeled by “ white ” answer , the bias of “ OOD should be different from ID ” enforces OOD-teacher to downplay “ white ” unfairly while ID-teacher does not . Case 3 : if ID ≈ OOD , then ID-teacher ≈ OOD-teacher . Learning is fair and the student should listen to both teachers equally . This case occurs when the training losses of the two are close . As shown in Figure 2 ( b ) , the ID-teacher and OOD-teacher produce similar predictions . The above introspection can be represented as a blended knowledge of the two teachers , which is distilled to the student model [ 18 ] . Yet , an unsolved challenge is how to obtain the “ oracle ” teachers , especially the OOD-teacher , because the OOD distribution is unseen in training , not mentioning to train a teacher model . Thanks to the recent causality-based approach [ 25 ] , we can approximate the OOD-teacher using a causal model that imagines the unseen world by counterfactual reasoning . Without loss of generality , we take visual QA and extractive QA as case studies . Experiments on VQA-CP [ 2 ] , VQA v2 [ 16 ] , and SQuAD [ 27 ] validate the effectiveness of our proposed IntroD . Interestingly , extensive ablations demonstrate that the success of IntroD is indeed from the causal introspection but not from the simple ensemble . 2 Related Work . Visual Question Answering ( VQA ) [ 5 , 3 , 16 ] is to answer the question given a visual context , i.e. , image . Traditional VQA models are found to exploit the language priors in the training data [ 16 , 2 , 20 ] . For example , in the first version of the VQA dataset VQA v1.0 , about 40 % of the sports- related questions are answered as “ tennis ” . Although utilizing the shortcut bias helps with the in-distribution ( ID ) performance , the out-of-distribution ( OOD ) one is severely hurt [ 2 ] . In order to mitigate the language bias , recent methods proposed to utilize extra annotations for accurate visual grounding [ 28 , 33 ] , generate synthetic data for data augmentation [ 7 , 1 , 14 , 30 , 31 ] , modifying language modules [ 19 , 23 ] , or explicitly formulate and exclude the language prior [ 6 , 11 , 25 ] . These methods obtain significant OOD improvement on the VQA-CP [ 2 ] dataset whose answer distributions in training and testing are reversed . However , the OOD improvement is achieved with the cost of a severe ID performance drop . Therefore , it is still a challenge to achieve strong performances in both ID and OOD evaluations . Extractive Question Answering ( extractive QA ) is to answer the question given a natural language context , i.e. , passage [ 27 ] . Extractive QA assumes that the answer always locates in the passage , and further reduces the generative QA task to a classification task , i.e. , position prediction . Recent years have witness many influential works [ 35 , 29 , 10 , 39 , 12 , 38 , 9 ] . However , directly predicting the answer positions has a severe side effect , i.e. , correlating answers with positions [ 22 ] . For example , if a language model is trained on a biased dataset where answers always locate in the first sentence of the passage , the model will tend to ground the answer in the first sentence . Recently , a new variant of the reading comprehensive dataset SQuAD [ 27 ] is proposed to evaluate whether language models are robust to the position bias [ 22 ] . Similar to VQA , the answer position distribution is skewed in the training set . In this paper , we follow Ko et al . [ 22 ] to evaluate the robustness for extractive QA . Ensemble-based methods for debiasing explicitly formulate and exclude the shortcut bias in the training data [ 6 , 11 , 7 , 25 , 8 ] . The shortcut bias can be captured by a separate branch [ 6 ] or statistical priors [ 11 ] . These methods are further interpreted as causality-based approaches [ 25 ] . However , most of these methods achieve promising performance under the out-of-distribution ( OOD ) evaluation but sacrifice the performance under the in-distribution ( ID ) evaluation . The reason is that these methods hold an assumption that the training and test distribution are very different or even reversed . In this paper , we implement our ID-teacher and OOD-teacher using the causality-based methods , and further achieve a good trade-off between ID and OOD evaluations . Previous OOD-teachers , i.e. , causality-based methods , only generate the OOD-prediction for debiased inference and ignore the role of ID-prediction . We further point out that the ID-prediction is crucial in introspecting the training process and achieving a good trade-off between ID performance and OOD performance . Knowledge Distillation is first proposed for model compression by transfering the teacher ’ s knowledge to a small student model [ 18 , 15 ] . The idea of knowledge distillation has been further extended to establish debiasing models in natural language understanding ( NLU ) tasks [ 32 , 13 ] and long-tail classification [ 34 , 42 , 17 ] . The idea of “ introspection ” is related to “ self distillation ” , which considers a student model itself as the teacher for the next training epoch or stage [ 24 , 41 , 21 , 36 , 40 ] . Although our introspection and self distillation both share the similar idea of “ self-teaching ” , they are fundamentally different : the latter is still in-distribution and has no comparative reasoning about the seen factual and unseen counterfactual . This difference reveals the key reason why introspection introduces new blended knowledge rather than just an old copy . Also , different from traditional knowledge distillation methods that use a fixed weight as hyper-parameter , our IntroD weights the models based on the introspective weights , which does not require a careful selection of hyper-parameters . 3 Introspective Distillation . We present a simple yet effective training paradigm , Introspective Distillation ( IntroD ) , to achieve a good trade-off between the in-distribution ( ID ) and out-of-distribution ( OOD ) performances for robust QA . Given a visual or natural language context C=c and a question Q=q as input , the QA model generates an answer A=a . Generally , the model is usually not prototyped as a generation but a multi-classification for prediction space reduction , i.e. , a ∈ A . For VQA [ 5 ] , the context refers to an image , and the answers are selected from a pre-defined candidate set . For extractive QA [ 27 ] , the context refers to a passage , and the answers are locations in it . Our IntroD aims to blend the ID and OOD inductive bias fairly . As illustrated in Figure 3 , it consists of three key parts : 1 ) causal teacher for capturing the ID and OOD inductive bias , 2 ) introspection for blending the two different inductive biases , and 3 ) distillation for a robust student model .

Summary: The paper claims to provide a better balance of in-domain and out-of-distribution settings. Normally, algorithms optimized for one hurt the other. The paper proposes to use a weighting mechanism to to balance a model's reliance on in-domain world (modeled as causal factual world) and out-of distribution world (modeled as causal counterfactual world). Experiments on VQA-CP and SQUAD datasets show that the proposed method does indeed help improve ID performance of several algorithms optimized of OOD accuracy while maintaining/slightly improving its OOD performance.