The Unreasonable Volatility of Neural Machine Translation Models (2020)

Abstract: Recent works have shown that Neural Machine Translation (NMT) models achieve impressive performance, however, questions about understanding the behavior of these models remain unanswered. We investigate the unexpected volatility of NMT models where the input is semantically and syntactically correct. We discover that with trivial modifications of source sentences, we can identify cases where unexpected changes happen in the translation and in the worst case lead to mistranslations. This volatile behavior of translating extremely similar sentences in surprisingly different ways highlights the underlying generalization problem of current NMT models. We find that both RNN and Transformer models display volatile behavior in 26% and 19% of sentence variations, respectively.

## introduction the performance of neural machine translation (nmt) models has dramatically improved in recent years, and with sufficient and clean data these models outperform more traditional models. challenges when sufficient data is not available include translations of rare words @xcite and idiomatic phrases @xcite as well as domain mismatches between training and testing @xcite . recently, several approaches investigated nmt models when encountering noisy input and how worst-case examples of noisy input can 'break' state-of-the-art nmt models @xcite . @xcite show that character-level noise in the input leads to poor translation performance. @xcite randomly insert words in different positions in the source sentence and observe that in in this paper, we explore unexpected and erroneous changes in the output of nmt models. consider the simple example in we investigate to what extent well-established modification sentence variations del some 500 years after the reformation, rome [now\φ φ φ] has a martin luther square. subnum i'm very pleased for it to have happened at newmarket because this is where i landed [30\31] years ago. ins i loved amy and she is [φ φ φ\also] the only person who ever loved me. subgen [he\she] received considerable appreciation and praise for this. nmt models are volatile during inference. specifically, we locally modify sentence pairs in the test set and identify examples where a trivial modification in the source sentence causes an 'unexpected change' in the translation. these modifications are generated conservatively to avoid insertion of any noise or rare words in the data (section 2.2). our goal is not to fool nmt models, but instead identify common cases where the models exhibit unexpected behaviour and in the worst cases result in incorrect translations. we observe that our modifications expose volatilities of both rnn and transformer translation models in 26% and 19% of sentence variations, respectively. our findings show how vulnerable current nmt models are to trivial linguistic variations, putting into question the generalizability of these models. ## evaluation of unexpected and erroneous changes the modifications described above generate sentences that are extremely similar and hence are expected to have a very similar difficulty of translation. we evaluate the nmt models on how robust and consistent they are in translating these sentence variations rather than their absolute quality. ## conclusion in this paper, we showed the unexpected volatility of nmt models by using a simple approach to modifying standard test sentences without introducing noise, i.e., by generating semantically and syntactically correct variations. we show that even with trivial linguistic modifications of source sentences we can effectively identify a surprising number of cases where the translations of extremely similar sentences are surprisingly different, see figure 1 . our manual analyses show that both rnn and transformer models exhibit volatile behaviour with changes in translation quality for 26% and 19% of sentence variations, respectively. this highlights the problem of generalizability of current nmt models and we hope that our insights will be useful for developing more robust nmt models.