# An Assistant System for Translation Flipped Classroom
## 1 Introduction
Current NMT has been able to extract this information well within a certain length. Situational context has put forward higher requirements on the reasoning and generalization ability of NMT. Cultural context is the most difficult to extract from the text, requiring an understanding of the background, such as social, cultural, economic, religious and political contexts. The context of these three levels restricts the production of the original text. Meanwhile, translators will be consciously or unconsciously influenced by their own ideology and purpose to understand and reproduce the meaning of the three levels [14]. To NMT, while sometimes being free from subjective influences is not a bad thing, it is difficult for a machine to simulate the \"faithfulness, expressiveness, and elegance\" of human translation without prior knowledge, such as culture and history. On the other hand, the development of NMT is inseparable from machine translation output (MTO) evaluation and the support of a large number of the high-quality corpus. Researchers need to track the progress of MT performance against accurate evaluation metrics, and the evaluation of MTO is exploited for improvements of translation models [15]. Lexical metrics [16], like BLEU [17], are widely used in the automatic evaluation of MTO. However, these metrics cannot represent lexical relationships very well and always impose strict identity matching, which results in low relevance to the human assessment of morphologically rich languages [18]. For low-resource languages, the corpus needs to be further collected and expanded, which is a long-term process. MT is believed to be able to help human translators efficiently perform language translation [19; 20; 21]. Studies have found that MT has excellent potential in translation training and can provide a new environment for translation teaching [22; 23]. There are more and more voices suggesting that translation students and graduates need to familiarise themselves with MT to ensure professional robustness and sustainability in the industry [24; 25; 26]. Some universities and private institutions have embraced MT as an innovative, pedagogically-enhanced tool to help learners become qualified translators and work efficiently in their practical lives [27; 28]. In education practice, teachers found that many students could not work effectively with MT, especially some who relied too much on it: phraseology of students' translation is easily influenced by MT; some students use MT outputs (MTO) directly as submissions to daily exercises. This greatly limits the improvement of students' translation ability. In order to solve the above problems, the translation flipped classroom assistant system (TFCAS) was developed. The system provides the corresponding MT result of the original sentence and asks the students to evaluate the quality of the MTO and give their own translation. Teachers can use the system to evaluate students' assignments, find common or typical problems and carry out targeted translation teaching. The function of the system also includes the collection of bilingual sentence pairs, which can be used for NMT model training, and the manual evaluation of MTOs, which can be used to guide further improvement of MT models. An Evaluation capability assessment algorithm is proposed to increase the dimensions of evaluating students' performance. There are several benefits of using this system: it reduces the burden of teachers in flipped classroom mode, helps students get used to working with MT and develop the habit of critically evaluating the output of MT, and it encourages both MT and translator training to support each other's sustainable development.