Checkpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems (2020)

Abstract: In this paper, we propose a method of re-ranking the outputs of Neural Machine Translation (NMT) systems. After the decoding process, we select a few last iteration outputs in the training process as the N -best list. After training a Neural Machine Translation (NMT) baseline system, it has been observed that these iteration outputs have an oracle score higher than baseline up to 1.01 BLEU points compared to the last iteration of the trained system.We come up with a ranking mechanism by solely focusing on the decoder’s ability to generate distinct tokens and without the usage of any language model or data. With this method, we achieved a translation improvement up to +0.16 BLEU points over baseline.We also evaluate our approach by applying the coverage penalty to the training process.In cases of moderate coverage penalty, the oracle scores are higher than the final iteration up to +0.99 BLEU points, and our algorithm gives an improvement up to +0.17 BLEU points.With excessive penalty, there is a decrease in translation quality compared to the baseline system. Still, an increase in oracle scores up to +1.30 is observed with the re-ranking algorithm giving an improvement up to +0.15 BLEU points is found in case of excessive penalty.The proposed re-ranking method is a generic one and can be extended to other language pairs as well.

## introduction neural machine translation(nmt) has brought excellent results in the field of machine @xcite ; @xcite ; @xcite due to generation of high-quality translations for different language pairs. yet even higher quality can be achieved by combining multiple models by techniques like ensembles hansen and @xcite and reranking @xcite . our work deals with how neural machine translation (nmt) can achieve better results explicitly with reranking methods. neural machine translation has an encoderdecoder architecture that is jointly trained to maximize the probability of target given source sentences. it first encodes the source sentence into a single vector, and the decoder predicts it.with the attention mechanism, it tries to apply weights to the input sentence at each time step. recent approaches like the transformer model @xcite have achieved the state-of-the-art results for machine translation. neural machine translation (nmt) however, leads to over-translation and under-translation as it tends to ignore the past alignment information, and it is effectively tackled by introducing a coverage vector @xcite . other approaches such as @xcite and @xcite too solve the coverage problem in nmt. without the coverage vector, it could result in a decrease in translation quality. we propose a method that selects a better hypothesis giving high importance to distinct words generated from decoder without the usage of any language model or data.after applying the proposed reranking method, an overall improvement in translation quality is observed as compared to the baseline system. the rest of the paper is organized as follows; section 2 discusses the work related to re-utilizing existing models for machine translation. section 3 describes our approach for checkpoint based reranking. in section 4, we present our reranking algorithm. in section 5, we demonstrate all of our experiments along with the results obtained, and finally, the paper is concluded in section 6 with future directions. ## related work the work of imamura and sumita (2017) explains the concepts of reranking and ensembling in detail. it introduces a method of bidirectional reranking in which it combines the hypothesis from l2r and r2l decoding following the works of @xcite , which proposes an agreement model to solve unbalanced outputs of recurrent neural networks. @xcite has introduced a reranking system that uses a smorgasbord of informative features in tasks where pbsmt and nmt produce translations of different quality. the work by @xcite shows how to apply perceptron-like reranking algorithms to improve the overall translation quality, and @xcite shows the usage of language models (lms) for reranking on hypotheses generated by phrase-based statistical machine translation systems. @xcite has shown linguistically motivated and computationally efficient structured language models for reranking in smt systems. the concept of checkpoint ensembles is introduced by @xcite and was later on improvised to independent ensembling @xcite . @xcite included a checkpoint averaging method for their model. @xcite has focused on decoding techniques that utilize existing models at parameter, word, and sentence level corresponding to checkpoint averaging, model ensembling, and candidate reranking and found that all of these improve the translation quality without retraining the model. ## checkpoint based reranking in our approach, the iteration outputs are selected as the n -best list. it implies for the last k iterations; we have the corresponding k-best list for a sentence. we take our oracle scores as the one that is having the largest bleu score @xcite on the test reference hypothesis from this k-best list. after obtaining the oracle scores from this k-best list, we observe that this score is larger than the baseline system, and it indicates that there is scope for further improvement of translation quality. so we propose a reranking method that improves the translation quality over the baseline system without any language model or data. we try to focus on the nature of translations that the decoder generates with and without coverage penalty. in the initial step, we keep track of the number of distinct words in the generated hypothe-sis, and the later ones we keep track of words that have repeated more than once.a higher score is given for sentences having a higher number of distinct tokens (d) and lower scores for those having more number of repetitive words (f ). for each sentence in the n -best list, these scores are sorted, and the sentence having the highest score is selected. this process is repeated for the entire test set, and the ones that are having the top most scores are chosen as the reranked output, as shown in section 4. ## reranking procedure algorithm 1 method input: translated target language sentences @xmath0...,h(n) ) at last k epochs for given sentence output: sentence having highest number of distinct words and lowest repetitive words for a sentence, freq is the count of each word; distinct is the total count of unique words. for each hypothesis in the k-best list we divide dis-tinct with freq and select the highest scorer. ## conclusions and future work in this paper, we introduce a method of selecting an n -best list for nmt systems and propose a way of reranking to the generated hypotheses from the system. we observe that our approach is giving better results over the baseline model by following the proposed reranking method and is also evaluated with the coverage penalty. one can investigate our approach with varying beam sizes and analyzing the effect of length penalty @xcite and comparing it with methods such as @xcite . we also look forward to coming up with better reranking ways that are closer to the oracle scores and investigate the efficacy of the approach in low-resourced data conditions. language models are used for getting the likelihood of sentences and is a widely used concept for reranking hypotheses. introducing language models during reranking could establish a tradeoff between perplexity and the scores to the hypotheses generated. we also plan to explore the work by c ¸aglar gülc ¸ehre et al. (2017) and c ¸aglar gülc ¸ehre et al. (2015) that introduces language models into the existing neural architecture with methods such as shallow fusion and deep fusion. it is another promising area to be looked upon for reranking.