Adversarial Subword Regularization for Robust Neural Machine Translation (2020)

Abstract: Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation as NMT models can experience various subword candidates. However, the diversification of subword segmentations mostly relies on the pre-trained subword language models from which erroneous segmentations of unseen words are less likely to be sampled. In this paper, we present adversarial subword regularization (ADVSR) to study whether gradient signals during training can be a substitute criterion for exposing diverse subword segmentations. We experimentally show that our model-based adversarial samples effectively encourage NMT models to be less sensitive to segmentation errors and improve the performance of NMT models in low-resource and out-domain datasets.

## introduction subword segmentation is a method of segmenting an input sentence into a sequence of subword units @xcite @xcite . segmenting a word to the composition of subwords alleviates the out-of-vocabulary problem while retaining encoded sequence length compactly. due to its effectiveness in the open vocabulary set, the method has been applied to many nlp tasks including neural machine translation (nmt) and others @xcite @xcite . recently, byte-pair-encoding(bpe) @xcite has become one of the de facto subword segmentation methods. however, as bpe deterministically segments each word into subword units, nmt models with bpe always observe the ## approach relying on the subword language models might bias nmt models to frequent segmentations, hence hinders the nmt model in understanding diverse segmentations. this may harm the translation quality of the nmt models when diverse morphological variations occur. however, simply exposing diverse segmentations uniformly leads to a decrease in performance @xcite . in this regard, we utilize gradient signals for exposing diverse, yet adversarial subword segmentation inputs for effectively regularizing nmt models. @xcite proposed to jointly learn to segment and translate by using hierarchical rnn @xcite , but the method is not model-agnostic and slow due to the increased sequence length of characterlevel inputs. on the other hand, our method is model-agnostic and operates on the word-level. our method seeks adversarial segmentations onthe-fly, thus the model chooses the subword candidates that are vulnerable to itself according to the state of the model at each training step. ## related work subword segmentation has been widely used as a standard in the nmt community since the byte-pair-encoding @xcite was proposed. @xcite introduced the training method of subword regularization. most recently, the bpedropout @xcite was introduced which modifies the original bpe's encoding process to enable stochastic segmentation. our work shares the motivation of exposing diverse subword candidates to the nmt models with previous works but differs in that our method uses gradient signals. other segmentation methods include word-piece @xcite and variable length encoding schme @xcite . also, there is another line of research that utilizes character-level segmentation @xcite @xcite . other works explored generating synthetic or natural noise for regularizing nmt models @xcite @xcite . @xcite introduced a dataset scraped from reddit for testing the nmt systems on the noisy text. recently, a shared task on building the robust nmt models was held @xcite . our method extends the adversarial training framework, which was initially developed in the vision domain @xcite and has begun to be adopted in the nlp domain recently @xcite @xcite @xcite @xcite . @xcite adopted the adversarial training framework on text classification by perturbing embedding space with continuous adversarial noise. @xcite introduced an adversarial training framework by discrete word replacements where candidates were generated from the language model. however, our method does not replace the word but replaces its subword composition. ## conclusions in this study, we propose adversarial subword regularization which samples subword segmentations that maximize the translation loss. segmentations from the subword language model might bias nmt models to frequent segmentations in the training set. on the other hand, our method regularizes the nmt models to be invariant to unseen segmentations. experimental results on low resource and out-domain datasets demonstrate the effectiveness of our method.