$k$-Neighbor Based Curriculum Sampling for Sequence Prediction

TL;DR

This paper introduces a curriculum learning method called Nearest-Neighbor Replacement Sampling to reduce exposure bias in language models, improving multi-step prediction accuracy by exploring similar token alternatives during training.

Contribution

It proposes a novel, simple, online sampling technique that replaces tokens with similar neighbors to enhance generalization and reduce errors in sequence prediction models.

Findings

Improves language model performance on benchmarks.

Enhances robustness when combined with scheduled sampling.

Requires minimal additional memory.

Abstract

Multi-step ahead prediction in language models is challenging due to the discrepancy between training and test time processes. At test time, a sequence predictor is required to make predictions given past predictions as the input, instead of the past targets that are provided during training. This difference, known as exposure bias, can lead to the compounding of errors along a generated sequence at test time. To improve generalization in neural language models and address compounding errors, we propose \textit{Nearest-Neighbor Replacement Sampling} -- a curriculum learning-based method that gradually changes an initially deterministic teacher policy to a stochastic policy. A token at a given time-step is replaced with a sampled nearest neighbor of the past target with a truncated probability proportional to the cosine similarity between the original word and its top $k$ most similar words. This allows the learner to explore alternatives when the current policy provided by the teacher is sub-optimal or difficult to learn from. The proposed method is straightforward, online and requires little additional memory requirements. We report our findings on two language modelling benchmarks and find that the proposed method further improves performance when used in conjunction with scheduled sampling.