Contrastive Token Learning with Similarity Decay for Repetition Suppression in Machine Translation
This work tackles the problem of monotonous and repetitive content generation in NMT, which is crucial for crosslingual conversation and trade, particularly for lengthy article and e-commerce descriptions.
This paper addresses the problem of textual repetition in Neural Machine Translation (NMT) by attributing it to elevated uncertainty in input text. The proposed Contrastive Token Learning with Similarity Decay (CTSD) algorithm dynamically suppresses tokens based on attention weights and inter-token distances, significantly outperforming existing approaches in precision and generalizability.
For crosslingual conversation and trade, Neural Machine Translation (NMT) is pivotal yet faces persistent challenges with monotony and repetition in generated content. Traditional solutions that rely on penalizing text redundancy or token reoccurrence have shown limited efficacy, particularly for lengthy article and e-commerce descriptions with inherent redundancy, even with the advent of Large Language Models (LLMs). This paper investigates the underlying causes of textual repetition through the lens of information entropy, attributing the phenomenon to the elevated uncertainty within the input text. To address this, a novel algorithm named Contrastive Token Learning with Similarity Decay (CTSD) is introduced, which modulates the suppression of tokens dynamically, informed by varying attention weights and inter-token distances. Furthermore, an e-commerce dataset comprised of title texts of online real items is compiled and released susceptible to hallucination translations to benchmark the algorithm. Extensive evaluations demonstrate that CTSD significantly outperforms existing approaches in precision and generalizability. Additional online A/B testing underscores its practical value, showing marked improvements in user engagement and conversion. Notably, this method has been implemented with full traffic on eight multilingual sites of alibaba.com, the largest B2B e-commerce platform in the world.