Normalizing Mutual Information for Robust Adaptive Training for Translation (2022)

Abstract: Despite the success of neural machine translation models, tensions between fluency of optimizing target language modeling and source-faithfulness remain as challenges. Previously, Conditional Bilingual Mutual Information (CBMI), a scoring metric for the importance of target sentences and tokens, was proposed to encourage fluent and faithful translations. The score is obtained by combining the probability from the translation model and the target language model, which is then used to assign different weights to losses from sentences and tokens. Meanwhile, we argue this metric is not properly normalized, for which we propose Normalized Pointwise Mutual Information (NPMI). NPMI utilizes an additional language model on source language to approximate the joint likelihood of source-target pair and the likelihood of the source, which is then used for normalizing the score. We showed that NPMI better captures the dependence between source-target and that NPMI-based token-level adaptive training brings improvements over baselines with empirical results from En-De, De-En, and En-Ro translation tasks.

## introduction neural machine translation (nmt) models have achieved remarkable performance since @xcite introduced encoder-decoder architecture with self-and cross-attention mechanisms. however, they were also reported to generate hallucinations @xcite in some cases, failing to balance the dual goals of improving fluency while preserving faithfulness to the source. conditional bilingual mutual information (cbmi), a metric for target tokens and sentences computed as the log quotient of the translation and the target-side language model probability, was while our proposed sentence-level npmi assigns a large score (near the upper bound 1) to the faithful sourcetarget pair and a small score (near zero, which indicates neutrality) to a rather noisy pair, sentence-level cbmi scores for the two pairs are unable to achieve that. note that the joint log likelihood values from the two pairs are comparable, while target lengths differ a lot. while cbmi score is devised to pursue this joint goal of translation, we argue that it does not take source context into account which leads to its failure to provide a reliable measure of relevance for some cases. for example, figure 1 illustrates faithful and noisy translations, where the former is expected to have a higher score. however, sentencelevel cbmi score, which is defined as aggregated token-level cbmi scores simply divided by the length of the target sentence, fails to capture that the first pair is much more strongly connected in terms of the content than the second one. we argue that this is because cbmi has a tendency to assign higher values for noisy or unlikely examples and vice versa due to the nature of pointwise mutual information (pmi) with an unbounded range. inspired by normalized pointwise mutual infor-mation (npmi), we propose to normalize by joint log likelihood (denoted as log p (src, tgt) in figure 1 ). our derivation of using npmi for sentenceand token-level weighting leads to a combination of log quotients of probabilities from translation model and source and target language models, unlike existing methods not considering the sourceside language model. figure 1 shows that our enhanced scoring, with a more founded derivation of paired normalization, can distinguish between faithful and noisy translations more clearly. our method is validated with wmt and iwslt benchmarks with diverse language pairs and shows consistent improvements in comet @xcite , a pretrained neural network based evaluation framework that reportedly correlates highly with human evaluation @xcite , as well as the widely-used bleu scores. ## method we first overview the concept of pmi and cbmi @xcite along with its proposed normalization. we then motivate why a new, and more systematic normalization is needed, and derive our proposed method. ## motivation: normalization our critiques for their proposed normalization are as follows: ## proposed: source-aware normalization we first propose an alternative normalization, inspired by npmi, then discuss how this score guides adaptive training. ## analysis here we present more detailed analyses of our method regarding how the different levels of weighting and dynamic weight smoothing over time affected the performance. all the results are from experiments conducted on iwslt14 de-en and evaluated on the test set. ## conclusion in this paper, we propose a source-aware metric for target tokens and sentences based on normalized pointwise mutual information (npmi) that effectively captures the dependence between the source and the target for translation task. with this score, the model can figure out how much specific tokens require the source context for proper translation and how faithful a given source-target pair is, thereby putting more focus on examples with higher adequacy or importance. we also devise a new token-level adaptive training strategy based on npmi score, which dynamically adjusts the participation of weighted loss over time to gracefully overcome the limitation of imprecise approximation of model output probabilities in the earlier training stage. experimental results on translation benchmarks show that our proposed npmi, combined with dynamic weight smoothing, performs well over various datasets and languages. we also validated through ablation experiments that our methods offer the best results when they are used together. we leave (1) the search for the best way of scheduling for weight smoothing and (2) leveraging powerful pretrained language models, rather than language models trained from scratch as future work.