Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space (2020)

Abstract: In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks. We treat the question data augmentation task as a constrained question rewriting problem to generate context-relevant, high-quality, and diverse question data samples. CRQDA utilizes a Transformer Autoencoder to map the original discrete question into a continuous embedding space. It then uses a pre-trained MRC model to revise the question representation iteratively with gradient-based optimization. Finally, the revised question representations are mapped back into the discrete space, which serve as additional question data. Comprehensive experiments on SQuAD 2.0, SQuAD 1.1 question generation, and QNLI tasks demonstrate the effectiveness of CRQDA.

## introduction data augmentation (da) is commonly used to improve the generalization ability and robustness of models by generating more training examples. compared with the da used in the fields of computer vision @xcite @xcite and speech processing @xcite , how to design effective da tailored to natural language processing (nlp) tasks remains a challenging problem. unlike the general image da techniques such as rotation and cropping, it is more difficult to synthesize new high-quality and diverse text. recently, some textual da techniques have been proposed for nlp, which mainly focus on text classification and machine translation tasks. one way is directly modifying the text data locally with word deleting, word order changing, and word replacement @xcite @xcite . another popular way is to utilize the generative model to generate new text data, such as back-translation @xcite , data noising technique @xcite , and utilizing pre-trained language generation model @xcite . machine reading comprehension (mrc) @xcite , question generation (qg) @xcite and, question-answering natural language inference (qnli) @xcite are receiving attention in nlp community. mrc requires the model to find the answer given a paragraph foot_0 and a question, while qg aims to generate the question for a given paragraph with or without a given answer. given a question and a sentence in the relevant paragraph, qnli requires the model to infer whether the sentence contains the answer to the question. because the above tasks require the model to reason about the question-paragraph pair, existing textual da methods that directly augment question or paragraph data alone may result in irrelevant question-paragraph pairs, which cannot improve the downstream model performance. question data augmentation (qda) aims to automatically generate context-relevant questions to further improve the model performance for the above tasks @xcite . existing qda methods mainly employ the round-trip consistency @xcite to synthesize answerable questions. however, the round-trip consistency method is not able to generate context-relevant unanswerable questions, where mrc with unanswerable questions is a challenging task @xcite . @xcite firstly study unanswerable question da, which relies on annotated plausible answer to constructs a small pseudo parallel corpus of answerable-to-unanswerable questions for unanswerable question generation. unfortunately, most question answering (qa) and mrc datasets do not provide such annotated plausible answers. inspired by the recent progress in controllable text revision and text attribute transfer @xcite , we propose a new qda method called controllable rewriting based question data augmentation (crqda), which can generate both new context-relevant answerable questions and unanswerable questions. the main idea of crqda is to treat the qda task as a constrained question rewriting problem. instead of revising discrete question directly, crqda aims to revise the original questions in a continuous embedding space under the guidance of a pretrained mrc model. there are two components of crqda: (i) a transformer-based autoencoder whose encoder maps the question into a latent representation. then its decoder reconstructs the question from the latent representation. (ii) a mrc model, which is pre-trained on the original dataset. this mrc model is used to tell us how to revise the question representation so that the reconstructed new question is a context-relevant unanswerable or answerable question. the original question is first mapped into a continuous embedding space. next, the pre-trained mrc model provides the guidance to revise the question representation iteratively with gradient-based optimization. finally, the revised question representations are mapped back into the discrete space, which act as the additional question data for training. in summary, our contributions are as follows: (1) we propose a novel controllable rewriting based qda method, which can generate additional highquality, context-relevant, and diverse answerable and unanswerable questions. (2) we compare the proposed crqda with state-of-the-art textual da methods on squad 2.0 dataset, and crqda outperforms all those strong baselines consistently. (3) in addition to mrc tasks, we further apply crqda to question generation and qnli tasks, and comprehensive experiments demonstrate its effectiveness. ## related works recently, textual data augmentation has attracted a lot of attention. one popular class of textual da methods is confined to locally modifying text in the discrete space to synthesize new data. @xcite propose a universal da technique for nlp called easy data augmentation (eda), which performs synonym replacement, random insertion, random swap, or random deletion operation to modify the original text. @xcite propose a word synonym replacement method with wordnet. @xcite relies on word paradigmatic relations. more recently, cbert @xcite retrofits bert @xcite to conditional bert to predict the masked tokens for word replacement. these da methods are mainly designed for the text classification tasks. unlike modifying a few local words, another commonly used textual da way is to use a generative model to generate the entire new textual samples, including using variational autoencodes (vaes) @xcite , generative adversarial networks (gans) (tanaka and aranha, 2019), and pretrained language generation models @xcite @xcite . back-translation @xcite is also a major way for textual da, which uses machine translation model to translate english sentences into another language (e.g., french), and back into english. besides, data noising techniques @xcite and paraphrasing @xcite are proposed to generate new textual samples. all the methods mentioned above usually generate individual sentences separately. for qda of mrc, qg, and qnli tasks, these da approaches cannot guarantee the generating question are relevant to the given paragraph. in order to generate contextrelevant answerable and unanswerable questions, our crqda method utilizes a pre-trained mrc as guidance to revise the question in continuous embedding space, which can be seen as a special constrained paraphrasing method for qda. question generation @xcite @xcite is attracting attention in the field of natural language generation (nlg). however, most previous works are not designed for qda. that is, they do not aim to generate context-relevant questions for improving downstream model performance. compared to qg, qda is relatively unexplored. recently, some works @xcite utilize round-trip consistency technique to synthesize answerable questions. they first use a generative model to generate the question with the paragraph and answer as model input, and then use a pre-trained mrc model to filter the synthetic question data. however, they are unable to generate context-relevant unanswerable questions. it should be noted that our method and round-trip consistency are orthogonal. crqda can also rewrite the synthetic question data by other methods to obtain new answerable and unanswerable question data. unanswerable qda is firstly explored in @xcite , which constructs a small pseudo parallel corpus of paired answerable and unanswerable questions and then generates relevant unanswerable questions in a supervised manner. this method relies on annotated plausible answers for the unanswerable questions, which does not exist in most qa and mrc datasets. instead, our method rewrites the original answerable question to a relevant unanswerable question in an unsupervised paradigm, which can also rewrite the original answerable question to another new relevant answerable question. our method is inspired by the recent progress on controllable text revision and text attribute transfer @xcite . however, our approach differs in several ways. first, those methods are used to transfer the attribute of the single sentence alone, but our method considers the given paragraph to rewrite the context-relevant question. second, existing methods jointly train an attribute classifier to revise the sentence representation, while our method unitizes a pre-trained mrc model that shares the embedding space with autoencoder as the guidance to revise the question representation. finally, the generated questions by our method serve as augmented data can benefit the downstream tasks. ## experiments in this section, we describe the experimental details and results. we first conduct the experiment on the squad 2.0 dataset @xcite to compare crqda with other strong baselines, which is reported in § 4.1. the ablation study and further analysis are provided in § 4.2. then we evaluate our method on additional two tasks including question generation on squad 1.1 dataset @xcite in § 4.3, and question-answering language inference on qnli dataset @xcite in § 4.4.