Iterative Paraphrastic Augmentation with Discriminative Span Alignment

Abstract

We introduce a novel paraphrastic augmentation 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 demonstrate our approach with experiments on the Berkeley FrameNet Project, a large-scale language 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 community 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 extrinsically evaluated in detail, showing positive results on a downstream task.

Neural semi-Markov CRF for Monolingual Word Alignment

“…Insertion Figure 1: An example that illustrates monolingual word alignment (shown as arrows) can support analysis of human editing process and training of text generation models ( §6.1), such as for simplifying complex sentences for children to read. (Culkin et al, 2021) when combined with paraphrase generation.…”

“…Tsujii (2017, 2020) utilized constituency parsers for compositional and non-compositional phrase alignments. Culkin et al (2021) considered span alignment for FrameNet (Baker et al, 1998) annotations and treated each span pair as independent prediction.…”