Clippers 1/18: Micha Elsner on neural inflection and rating with analogical candidates

Neural inflection and rating with analogical candidates (joint w/Andrea Sims)

Abstract: Recent research on computational inflection prediction leads to a frustrating quandary. On the one hand, neural sequence-to-sequence models (Kann and Schuetze, 2016) provide steadily-improving state of the art perfor mance in predicting the inflectional forms of real words, outperforming a variety of non-neural models proposed in previous work (Nicolai et al., 2016). On the other, a series of experiments reveal their inadequacy in predicting the acceptability ratings of “wug” nonce words (Corkery et al., 2019). Like other neural models, these systems sometimes learn brittle generalizations which differ from human cognition and fail badly on out-of-sample data (Dankers et al., 2021). We present a neural system which aims to obtain the best of both worlds: state-of-the-art inflection prediction performance, and the ability to rate a wide variety of plausible forms for a given input in a human-like way. We show that, unlike many pre-neural models, the system is capable of generalizing across classes of related inflectional changes, leading to new testable hypotheses about the mental representation of inflectional paradigms.