A Representation Level Analysis of NMT Model Robustness to Grammatical Errors

TL;DR

This paper investigates how neural machine translation models internally handle grammatical errors by analyzing their representations and attention mechanisms, revealing a process of error detection and correction within model layers.

Contribution

It introduces a representation-level analysis of NMT robustness, identifying the role of Robustness Heads and their impact on handling grammatical errors.

Findings

Encoder detects and corrects grammatical errors through internal representations.

Robustness Heads attend to linguistic units relevant to errors.

Fine-tuning enhances reliance on Robustness Heads for correction.

Abstract

Understanding robustness is essential for building reliable NLP systems. Unfortunately, in the context of machine translation, previous work mainly focused on documenting robustness failures or improving robustness. In contrast, we study robustness from a model representation perspective by looking at internal model representations of ungrammatical inputs and how they evolve through model layers. For this purpose, we perform Grammatical Error Detection (GED) probing and representational similarity analysis. Our findings indicate that the encoder first detects the grammatical error, then corrects it by moving its representation toward the correct form. To understand what contributes to this process, we turn to the attention mechanism where we identify what we term Robustness Heads. We find that Robustness Heads attend to interpretable linguistic units when responding to grammatical errors, and that when we fine-tune models for robustness, they tend to rely more on Robustness Heads for updating the ungrammatical word representation.