What do Models Learn from Question Answering Datasets? (2020)

Abstract: While models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering itself. In this paper, we investigate if models are learning reading comprehension from QA datasets by evaluating BERT-based models across five datasets. We evaluate models on their generalizability to out-of-domain examples, responses to missing or incorrect data, and ability to handle question variations. We find that no single dataset is robust to all of our experiments and identify shortcomings in both datasets and evaluation methods. Following our analysis, we make recommendations for building future QA datasets that better evaluate the task of question answering through reading comprehension. We also release code to convert QA datasets to a shared format for easier experimentation at https://github.com/amazon-research/qa-dataset-converter

## introduction question answering (qa) through reading comprehension has seen considerable progress in recent years. this progress is in large part due to the release of large language models like bert @xcite and new datasets that have introduced impossible questions @xcite , bigger scales @xcite , and context @xcite to question answering. at the time of writing this paper, models have outperformed human baselines on the widely-used squad 1.1 and squad 2.0 datasets, and more challenging datasets such as quac have models less than 7 f1 points away. despite these increases in f1 scores, we are still far from saying question answering is a solved problem. concerns have been raised over how challenging qa datasets are. previous work has found that simple heuristics can give good performance on squad 1.1 @xcite , and successful squad models lack robustness by giving inconsistent answers @xcite or being vulnerable to adversarial attacks @xcite . if state-of-the-art models are excelling at test sets but not solving the underlying task of reading comprehension, then our test sets are flawed. we need to better understand what models learn from qa datasets. in this work, we ask three questions: (1) does performance on individual qa datasets generalize to new datasets? (2) do models need to learn reading comprehension for qa datasets?, and (3) can qa models handle variations in questions? to answer these questions, we evaluate bert models trained on five qa datasets using simple generalization and robustness probes. we find that (1) model performance does not generalize well outside of heuristics like question-context overlaps, (2) removing or corrupting dataset examples does not always harm model performance, showing that models can rely on simpler methods than reading comprehension to answer questions, and (3) no dataset fully prepares models to handle question variations like filler words or negation. these findings suggest that while qa models can learn heuristics around question-context overlaps and named entities, they do not need to learn reading comprehension to perform well on qa datasets. based on these findings, we make recommendations on how to better create and evaluate qa datasets. ## related work our work is inspired by recent trends in nlp to evaluate generalizability and probe what models learn from datasets. in terms of generalizability, prior work has been done by @xcite who evaluated a squad 1.1 model across four datasets and @xcite , who comprehensively evaluated ten qa datasets. the mrqa 2019 shared task @xcite evaluated transferability across multiple datasets, and @xcite proposed a method to train one model on 17 different qa datasets. in our work, we focus on question answering through reading comprehension and extend the work on generalizability by including impossible questions in all our datasets and analyzing the effects of questioncontext overlap on generalizability. there is also growing interest in probing what models learn from datasets @xcite @xcite . previous work in question answering has found that state-of-the-art models can get good performance on incomplete input @xcite @xcite , under-rely on important words, @xcite , and over-rely on simple heuristics @xcite . experiments on squad in particular have shown that squad models are vulnerable to adversarial attacks @xcite and not robust to paraphrases @xcite . our work continues exploring what models learn by comprehensively testing multiple qa datasets against a variety of simple but informative probes. we take inspiration from previous studies, and we make novel contributions by using bert, a stateof-the-art model, and running several experiments against five different qa datasets to investigate the progress made in reading comprehension. the question is about a document and the answer is either an extracted span of text or labeled unanswerable. to consistently compare and experiment across models, we convert all datasets into a squad 2.0 json format. 1 since most datasets have a hidden test set, we evaluate models on the dev set and consequently refer to the dev sets as test sets in this paper. the train and dev sets sizes are shown in ## datasets squad 2.0 @xcite consists of 150k question-answer pairs on wikipedia articles. to create squad 1.1, crowd workers wrote questions about a wikipedia paragraph and highlighted the answer @xcite . squad 2.0 includes an additional 50k plausible but unanswerable questions written by crowd workers. triviaqa @xcite includes 95k question-answer pairs from trivia websites. the questions were written by trivia enthusiasts and the evidence documents were retrieved by the authors retrospectively. we use the variant of triviaqa where the documents are wikipedia articles. natural questions (nq) @xcite ) contains 300k questions from google search logs. for each question, a crowd worker found a long and short answer on a wikipedia page. we use the subset of nq with a long answer and frame the task as finding the short answer in the long answer. we only include examples with answers in the paragraph text (as opposed to a table or list). newsqa @xcite ) contains 100k questions on 10k cnn articles. one set of crowd workers wrote questions based on a headline and summary, and a second set of workers found the answer in the article. we reintroduce unanswerable questions that were excluded in the original paper. there are notable differences among our datasets in terms of genre and how they were built. in ## model in ## experiments in this section, we discuss the experiments run to evaluate what models learn from qa datasets. all results are reported as f1 scores since they are correlated with exact match scores and are more forgiving to sometimes arbitrary cutoffs of answers (for example, we prefer to give some credit to a model for selecting "charles iii" even if the answer was "king charles iii"). ## conclusions in this work, we probed five qa datasets with six tasks and found that our models did not learn to generalize well, remained suspiciously robust to incorrect or missing data, and failed to handle variations in questions. these findings show that models learn simple heuristics around question-context overlap or entity types and pick up on underlying patterns in the datasets that allow them to remain robust to corrupt examples but not to valid variations. the shortcomings in datasets and evaluation methods make it difficult to judge if models are learning reading comprehension. based on our work, we make the following recommendations to researchers who create or evaluate qa datasets: