Introducing Mouse Actions into Interactive-Predictive Neural Machine Translation (2021)

Abstract: The quality of the translations generated by Machine Translation (MT) systems has highly improved through the years and but we are still far away to obtain fully automatic high-quality translations. To generate them and translators make use of Computer-Assisted Translation (CAT) tools and among which we find the Interactive-Predictive Machine Translation (IPMT) systems. In this paper and we use bandit feedback as the main and only information needed to generate new predictions that correct the previous translations. The application of bandit feedback reduces significantly the number of words that the translator need to type in an IPMT session. In conclusion and the use of this technique saves useful time and effort to translators and its performance improves with the future advances in MT and so we recommend its application in the actuals IPMT systems.

## introduction in recent years there had been a large number of advances in the machine translation (mt) field that has led to a significant improvement in the quality of the translations. currently, even with all the new advances, the mt systems are still not able to generate perfect ready to use translations @xcite . indeed, mt systems usually require human post-editing in order to achieve perfect translations. the computer-assisted translation (cat) tools aim to generate high-quality translations using the knowledge and experience of professional translators while reducing the effort that they need to do. there is a large variety of cat tools approaches, among which we focus on the interactive-predictive machine translation (ipmt) systems. some of the recent projects in this field are transtype @xcite @xcite , matecat @xcite , casmacat @xcite @xcite and mmpe @xcite . they aim to create a workbench with an array of innovative features that were not available in other tools when they started. ipmt is one of the main paradigms that include these projects, where an expert translator provides feedback to the system, typically using the keyboard and mouse, to generate new predictions that correct previous errors. there are two main ipmt approaches, both use usually the keyboard and mouse as the main feedback interface, but the validation process changes between prefix @xcite and segments @xcite . in this project, we use the validation by prefix approach. figure 1 illustrates a conventional ipmt session. initially, the user is provided with a source sentence x to be translated. at iteration 0, the ipmt system generates the first source (x): una versión traducida de un texto. ## related work the reduction of the effort needed in the translation process is a problem that has been thoroughly studied, resulting in a large variety of approaches. some projects have investigated which information and display are more useful to the users, like showing the word alignment information @xcite , setting a maximum length for the predictions displayed @xcite or just using touch-based actions @xcite . other approaches reduce the effort that the user has to do more directly: using confidence measures to reduce the number of words to check @xcite , autocompleting the predictions typed by the user @xcite , or adding new input information to the system reduces the human effort of generating a new prediction @xcite . there are also projects like @xcite that investigated how to reduce the human effort in an ipmt system using reinforcement learning. this technique lets them use new kinds of feedback to the system that they use as a reward to adjust the parameters of the model and obtain better translations. in the paper, we take the approach introduced by @xcite , demonstrating that with only the error position, the interactive-predictive statistical machine translation (ipsmt) systems are capable of correct their translations. we apply and implement this technique on an interactive-predictive neural machine translation (ipnmt) system, obtaining a higher reduction in the human effort. ## interactive-predictive neural mt in this section, we see briefly the ipnmt framework. first of all, we have to see the general framework of the neural machine translation (nmt) models that we use to understand how the translations are created and how we later add human feedback to the equation. this framework was introduced by @xcite and has demonstrated its power in the last years @xcite . given a sentence x j 1 = x 1 , ..., x j from the source language x, to find the sentence ŷ î 1 = ŷ1 , ..., ŷî from the target language y , that has the highest probability of being the translation of x j 1 , the fundamental equation of the statistical approach to nmt would be: the ipnmt framework adds the feedback generated by the human to equation (1) to help with the translation process. when the expert translator finds an error in position p, moves the cursor and types the correct word, producing the feedback f p 1 = f 1 , ..., f p where f p is the word that the user has typed to correct the error. we add the feedback with the last generated hypothesis to equation (1): ## enriching user-machine interaction until now, the only interface that we have explored to ipmt is the combination of keyboard and mouse. the ipmt system provides a translation, and the user corrects it by placing the cursor before the first error and typing the correct word. in this paper, we retake the work introduced by @xcite . we use the mouse as an interface for the user-machine interaction to provide the ipmt system the information about the position of the first error. first of all, we have to consider the two different classes of actions that can be performed with the mouse, non-explicit mouse actions (mas) and interaction-explicit mas.