A Dual Reinforcement Method for Data Augmentation using Middle Sentences for Machine Translation (2023)

Abstract: This paper presents an approach to enhance the quality of machine translation by leveraging middle sentences as pivot points and employing dual reinforcement learning. Conventional methods for generating parallel sentence pairs for machine translation rely on parallel corpora, which may be scarce, resulting in limitations in translation quality. In contrast, our proposed method entails training two machine translation models in opposite directions, utilizing the middle sentence as a bridge for a virtuous feedback loop between the two models. This feedback loop resembles reinforcement learning, facilitating the models to make informed decisions based on mutual feedback. Experimental results substantiate that our proposed method significantly improves machine translation quality.

## introduction the accuracy of neural machine translation is limited by the quantity of available training data @xcite , leading to the development of various techniques for data augmentation. in this paper, we propose a novel method that leverages middle sentences @xcite as pivot points and uses dual reinforcement learning @xcite for data augmentation in machine translation. dual learning @xcite @xcite entails the concurrent training of two neural networks, to enhance translation accuracy by leveraging the reconstruction model's ability to generate synthetic parallel sentence pairs. data augmentation involves artificially augmenting the size of the training data by generating additional sentence pairs through diverse techniques, such as back-translation @xcite @xcite . these techniques offer potential solutions to mitigate the scarcity of parallel corpora and improve the quality of machine translation models by providing supplementary training data. in our proposal, we aim to combine the strengths of dual learning and data augmentation with the use of middle sentences as pivot points to reinforce the training process and further enhance the accuracy of the machine translation model. we start by presenting our dual reinforcement method in section 2. we present our experiment setup in section 3 and results in section 4. ## methods our method combines the use of a dual learning framework with data augmentation techniques, leveraging the middle sentences of parallel sentence pairs as pivot points. the general process involves generating additional parallel sentence pairs through middle sentence generation, using the middle sentences to create new sentence pairs and refining the translations using a machine translation model. this process is repeated iteratively, forming a reinforcement loop that enhances the quality of the translation model through synthetic data, i.e., middle sentences. in the following subsections, we provide a detailed explanation of each step in our method. ## experimental setup the experimental setup for this study uses a neural machine translation (nmt) model avaiable in the opennmt tool @xcite . the selected architecture is a transformer encoder and decoder, with a word vector size of 512, 6 layers, and 8 heads, alongside an rnn size of 512. the transformer feed-forward network has a size of 2048. during training, gradients are accumulated over 8 batches, and the model is optimized using the adam optimizer with beta1 set to 0.9, beta2 set to 0.998, and a learning rate of 0.001. batch sizes are set to 4096, utilizing token batch type, with token normalization and a dropout rate of 0.1, while label smoothing was set to 0.1. we employ a parallel dataset in english and chinese extracted from tatoeba to evaluate our system's performance, we use three standard metrics: bleu (bilingual evaluation understudy), chrf (character-level f-score), and ter (translation error rate). bleu @xcite quantifies the n-gram overlap between the generated text and the reference text. chrf @xcite calculates the character n-gram f-score between the generated and reference text. finally, ter @xcite measures the minimum edit distance between the generated and reference text, accounting for insertions, deletions, and substitutions. furthermore, we use sacrebleu @xcite to conduct significance testing, and highlight the experimental outcomes that exhibited a significant improvement by bolding them. ## conclusion this paper presented a novel data augmentation method for enhancing machine translation performance by using middle sentences and dual learning. our approach aims to overcome the (a) en-zh machine translation model (b) zh-en machine translation model figure 5: bleu scores for various thresholds of euclidean distance