Iterative Paraphrastic Augmentation with Discriminative Span Alignment

Iterative Paraphrastic Augmentation with Discriminative Span Alignment Ryan Culkin J. Edward Hu Elias Stengel-Eskin Guanghui Qin Benjamin Van Durme Johns Hopkins University {rculkin, edward.hu, elias, gqin, vandurme}@jhu.edu Abstract We introduce a novel paraphrastic augmenta- tion strategy based on sentence-level lexically constrained paraphrasing and discriminative span alignment. Our approach allows for the large-scale expansion of existing datasets or the rapid creation of new datasets using a small, manually produced seed corpus. We demon- strate our approach with experiments on the Berkeley FrameNet Project, a large-scale lan- guage understanding effort spanning more than two decades of human labor. With four days of training data collection for a span alignment model and one day of parallel compute, we automatically generate and release to the com- munity 495,300 unique (Frame, Trigger) pairs in diverse sentential contexts, a roughly 50-fold expansion atop FrameNet v1.7. The resulting dataset is intrinsically and extrin- sically evaluated in detail, showing positive results on a downstream task. 1 Introduction Data augmentation is the process of automatically increasing the size or diversity of a dataset with the goal of improving performance on a task of interest. It has been applied in many areas of machine learning including computer vision (Shorten and Khoshgoftaar, 2019) and speech recognition (Ragni et al., 2014; Ko et al., 2015). With text-based datasets in particular, para- phrastic augmentation, a technique to automat- ically expand datasets in their overall size and lexico-syntactic diversity via the use of a para- phrase model, may be applied. In general, a paraphrase model outputs a sentence S′ given an input sentence S such that meaning(S) ≈ meaning(S′) and S ̸= S′. Prior work has demonstrated that paraphrastically augmented datasets are beneficial when applied to a vari- ety of sentence-level tasks including machine translation, natural language inference, and intent classification (Ribeiro et al., 2018; Hu et al., 2019a; Kumar et al., 2019). Often in paraphrastic augmentation an input sentence is rewritten one or more times, with the assumption the transformed sentence(s) preserve the original label. For example, in sentiment analysis, data consists of (Sentencei, Labeli) pairs, where each Labeli is in {0, 1}, indicat- ing negative or positive sentiment. To augment this kind of dataset, we can paraphrase each Sentencei with a model f and thereby produce an additional (f(Sentencei), Labeli) pair, doubling the size of the dataset. In many natural language understanding tasks, however, data contains span labels of the form: (Sentencei, {(starti,1, endi,1, typei,1), ...}), where the latter element is a set of tuples indicating each label’s location (as a contiguous subsequence of the input tokens) and type. In this paper, we develop a data augmentation strategy for span labeling problems where we are concerned with balancing the joint objectives of finding different ways to express meaning at the level of a word or phrase while ensuring the paraphrase is sensitive to the context of the surrounding sentence. Although a paraphrase is expected to have the same meaning as the sentence from which it was generated, words and phrases are usually …

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