Domain adaptation allows generative language models to address specific flaws caused by the domain shift of their application. However, the traditional adaptation by further training on in-domain data rapidly weakens the model's ability to generalize to other domains, making the open-ended deployments of the adapted models prone to errors. This work introduces novel training objectives built upon a semantic similarity of the predicted tokens to the reference. Our results show that (1) avoiding the common assumption of a single correct prediction by constructing the training target from tokens' semantic similarity can mitigate catastrophic forgetting during domain adaptation, while (2) preserving the quality of the adaptation, (3) with negligible additions to compute costs. In the broader perspective, the objectives grounded in a soft token alignment pioneer the exploration of the middle ground between the efficient but naive exact-match token-level objectives and expressive but computationally- and resource-intensive sequential objectives.

这项研究介绍了建立在预测令牌语义相似度的新型训练目标，通过避免单个正确预测的假设，可以减轻域自适应中的灾难性遗忘，同时保持适应质量，并且计算成本几乎可以忽略不计。

机器翻译中鲁棒性适应的软对齐目标