Token-level Fitting Issues of Seq2seq Models

Guangsheng Bao, Yue Zhang

Keywords: overfitting, underfitting, seq2seq model

TL;DR: We find that seq2seq models trained with early-stopping suffer from overfitting and underfitting at the token level. We identify three major factors that influence token-level fitting.

Abstract: Sequence-to-sequence (seq2seq) models have been widely used for natural language process, computer vision, and other deep learning tasks. We find that seq2seq models trained with early-stopping suffer from issues at the token level. In particular, while some tokens in the vocabulary demonstrate overfitting, others underfit when training is stopped. Experiments show that the phenomena are pervasive in different models, even in fine-tuned large pretrained-models. We identify three major factors that influence token-level fitting, which include token frequency, parts-of-speech, and prediction discrepancy. Further, we find that external factors such as language, model size, domain, data scale, and pretraining can also influence the fitting of tokens.

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