# Neural Models for Measuring Confidence on Interactive Machine Translation Systems
## 2 Literature Review  
The first modern approach to MT dates back to 1949 by [PERSON] [23]. From then, researchers thought that they would obtain fully automatic high-quality translations in a few years, but that was not the case. In 1966, the Automatic Language Processing Advisory Committee (ALPAC) [24] published a report that concluded that MT was more expensive, less accurate, and slower than human translation and was not likely to reach the high quality sought soon. Even now, with all the improvements made in the MT field, the translations generated are not perfect, and in most cases, the system requires a professional translator to obtain high quality.  
In order to obtain high-quality translations, in the beginning, the users had to correct the sentences generated by the machines without any help. Over time, researchers developed new tools to assist humans in the translation process and sped it up, such as the computer-assisted translation (CAT) tools [25]. Between some of the most used CAT tools, we can find translation memories, language search-engines, post-editors, and IPMT systems. Interactive-predictive machine translation (IPMT) focuses on the interaction between the translator and the machine. The MT system generates a hypothesis with the available information, and the user provides feedback to the system to correct it if necessary. [PERSON] et al. (2009) [26] firstly introduced this concept.  
Researchers continued investigating different techniques to assist the translators in IPMT systems. Projects such as CasMaCat [27] and TransType2 [28] appeared as working environments for translators that incorporated an array of these innovative techniques that were not available in other tools at the time. They combine techniques such as intelligent autocompletion [26], confidence measures [18], prediction length control [29], search and replace, word alignment information [30], and prediction rejection [31]. These projects were one of the first times CMs appeared in CAT tools.  
In 2012, [PERSON] and [PERSON] started to organize the Quality Estimation shared task at the Workshop on Machine Translation (WMT) [32]. This shared task helps establish the state-of-the-art performance in the field. It also enables researchers to determine new and effective quality indicators and identify alternative machine learning techniques for the problem. Although it started only with sentence-level CMs, nowadays it also uses CMs at the word level.  
The investigation in the field increased, and researchers developed new projects and frameworks. The QuEst open-source framework [6] appeared after the first shared task on quality estimation and used the results obtained there. Between others, they implemented the most commonly used features in the task.  
The CM field also changed with the apparition of neural models in the MT field. The CM models extracted some of the features used from the statistical MT models, so this paradigm change transformed the techniques and features used in the field. Researchers could now extract new features from the neural MT models and train neural models to predict confidence estimations directly. With these new possibilities, the frameworks OpenKiwi [33] and DeepQuest [8] appeared. Both perform estimations at a sentence and word level, and DeepQuest also allows document level.  
Nowadays, CMs are primarily used in post-edition, and researchers study their applicability in this environment. Some projects analyze the relationship between the quality of the translations and the time needed from using CMs in this environment with actual cases [13; 19]. The IPMT environment is less used and proposes a new problem as the system performs the estimations simultaneously with the translation generation, so the system does not have the complete translation to calculate the word CMs. Similar to our project, there are still works that continue the investigation in this sub-field [7; 34].