Inducing Character-level Structure in Subword-based Language Models with Type-level Interchange Intervention Training
This addresses the problem of handling character-level manipulations in language tasks for NLP researchers and practitioners, representing a novel method for a known bottleneck.
The paper tackled the challenge of character-level tasks for subword-based language models by developing a causal intervention framework to learn robust and interpretable character representations, resulting in outperforming character-level models on complex tasks like spelling correction in context and improving robustness on unseen token sequences.
Language tasks involving character-level manipulations (e.g., spelling corrections, arithmetic operations, word games) are challenging for models operating on subword units. To address this, we develop a causal intervention framework to learn robust and interpretable character representations inside subword-based language models. Our method treats each character as a typed variable in a causal model and learns such causal structures by adapting the interchange intervention training method of Geiger et al. (2021). We additionally introduce a suite of character-level tasks that systematically vary in their dependence on meaning and sequence-level context. While character-level models still perform best on purely form-based tasks like string reversal, our method outperforms character-level models on more complex tasks that blend form, meaning, and context, such as spelling correction in context and word search games. Compared with standard subword-based models, our approach also significantly improves robustness on unseen token sequences and leads to human-interpretable internal representations of characters.