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: This paper studies learning models for visual and text-only question answering (VQA and SQUAD) that do well on both in-distribution test sets, as well as out-of-distribution ones.  The paper takes a knowledge distillation approach for this and designs two "teacher" modules: "ID-teacher" that captures in-domain bias, and "OOD-teacher" in which the in-domain bias is reduced using [23] (Niu et al 2021)'s method. The models are then ensembled and the aggregate probabilities are used to supervise training of a student model.   The paper evaluates this approach on VQA and SQUAD, with a variety of different methods as teachers. The method seems to increase performance on both OOD and ID sets (which is perhaps surprising, since one might hypothesize that the ID teacher ought to do best by itself on ID data, and likewise for the OOD teacher).