Probing Causes of Hallucinations in Neural Machine Translations
This addresses a critical issue in NMT for improving translation reliability, though it is incremental as it builds on existing probing methods.
The paper investigates the causes of hallucinations in Neural Machine Translation, finding that deficiencies in the encoder, particularly embeddings, and vulnerable cross-attentions are key factors, with cross-attention also mitigating some encoder errors.
Hallucination, one kind of pathological translations that bothers Neural Machine Translation, has recently drawn much attention. In simple terms, hallucinated translations are fluent sentences but barely related to source inputs. Arguably, it remains an open problem how hallucination occurs. In this paper, we propose to use probing methods to investigate the causes of hallucinations from the perspective of model architecture, aiming to avoid such problems in future architecture designs. By conducting experiments over various NMT datasets, we find that hallucination is often accompanied by the deficient encoder, especially embeddings, and vulnerable cross-attentions, while, interestingly, cross-attention mitigates some errors caused by the encoder.