Touch Editing: A Flexible One-Time Interaction Approach for Translation (2020)

Abstract: We propose a touch-based editing method for translation, which is more flexible than traditional keyboard-mouse-based translation postediting. This approach relies on touch actions that users perform to indicate translation errors. We present a dual-encoder model to handle the actions and generate refined translations. To mimic the user feedback, we adopt the TER algorithm comparing between draft translations and references to automatically extract the simulated actions for training data construction. Experiments on translation datasets with simulated editing actions show that our method significantly improves original translation of Transformer (up to 25.31 BLEU) and outperforms existing interactive translation methods (up to 16.64 BLEU). We also conduct experiments on post-editing dataset to further prove the robustness and effectiveness of our method.

## introduction neural machine translation (nmt) has made great success during the past few years @xcite @xcite , but automatic machine translation is still far from perfect and cannot meet the strict requirements of users in real applications @xcite . many notable humanmachine interaction approaches have been proposed for allowing professional translators to improve machine translation results @xcite @xcite . as an instance of such approaches, post-editing directly requires translators to modify outputs from machine translation @xcite . however, traditional post-editing requires intensive keyboard interaction, which is inconvenient on mobile devices. @xcite suggest a one-time interaction approach with lightweight editing ef-forts, quickedit, in which users are asked to simply mark incorrect words in a translation hypothesis for one time in the hope that the system will change them. quickedit delivers appealing improvements on draft hypotheses while maintaining the flexibility of human-machine interaction. unfortunately, only marking incorrect words is far from adequate: for example, it does not indicate the missing information beyond the original hypothesis, which is a typical issue called under-translation in machine translation @xcite . in this paper, we propose a novel one-time interaction method called touch editing, which is flexible for users and more adequate for a system to generate better translations. inspired by human editing process, the proposed method relies on a series of touch-based actions including substitu-tion, deletion, insertion and reordering. these actions do not include lexical information and thus can be flexibly provided by users through various of gestures on touch screen devices. by using these actions, our method is able to capture the editing intention from users to generate better translations: for instance, insertion indicates a word is missing at a particular position, and our method is expected to insert the correct word. to this end, we present a neural network model by augmenting transformer @xcite with an extra encoder for a hypothesis and its actions. since it is impractical to manually annotate large-scale action dataset to train the model, we thereby adopt the algorithm of ter @xcite to automatically extract actions from a draft hypothesis and its reference. to evaluate our method, we conduct simulated experiments on translation datasets the same as in other works @xcite , the results demonstrate that our method can address the well-known challenging issues in machine translation including over- quickedit hypothesis y' travel far does not necessary to proctor for food supply . result travel far does not require to proctor food supplies . source x weite wege müsse proctor für die nahrungsmittelbeschaffung nicht gehen . reference y proctor does not have to travel far to buy food . touch editing hypothesis y' travel far does not necessary to proctor for food supply . modified 𝑚𝑚(y') proctor does not necessary to travel far @xmath0. action sequence a ---s ---s i -d -result proctor does not have to travel far to buy food . translation, under-translation and mis-ordering, and thus it outperforms transformer and quickedit by a margin up to 25.31 and 16.64 bleu points respectively. in addition, experiments on post-editing dataset further prove the effectiveness and robustness of our method. finally, we implement a real application on mobile phones to discuss the usability in real senarios. ## automatic data annotation the actions we defined in section 2.1 can be provided by human translators in real applications. however, it is impractical to manually collect a large scale annotated dataset for training our model. thus we resort to propose an approach to automatically extract editing actions from a machine translation hypothesis and its corresponding reference. to make our method powerful, the number of editing actions which convert a hypothesis to its algorithm 1 extracting actions with ter input: hypothesis y , reference y m(y ) ← y a ← empty action sequence repeat find reordering r that most reduces min-editdistance(m(y ), y) if r reduces edit distance then m(y ) ← applying r to m(y ) end if until no beneficial reordering remains a ← min-edit(m(y ), y) m(y ) ← insert ins into m(y ) based on a output: m(y ), a reference is minimal as presented in section 2.1. @xcite study this problem and point out that its optimal solution is np-hard @xcite . to optimize the number of editing actions, they instead propose an approximate algorithm based on minimal edit distance. the basic idea of their algorithm can be explained as follows. it repeatedly modifies the intermediate string by applying reordering actions which is greedily found to mostly reduce the edit distance between the intermediate string and the reference, until no more beneficial reordering remains. in this paper, we adopt the basic idea of @xcite to automatically extract actions. as shown in algorithm 1, given a reference and a hypothesis, the algorithm repeatedly reorders words to reduce the word-level minimal edit distance between reference y and modified hypothesis m(y ) until no beneficial reordering remains. with the modified hypothesis m(y ), the algorithm then calculates the editing action sequence a that minimize the word-level edit distance between m(y ) and y (see action sequence a in figure 1 ). it finally inserts special token ins to keep alignment between the modified hypothesis and the action sequence (see modified m(y ) in figure 1 ). the output of the algorithm, which is a tuple of modified hypothesis and action sequence, together with the source sentence and its reference, are used to train our model as described in section 2.2. ## experiment we conduct simulated experiment on translation datasets. specifically, we translate the source sentences in translation datasets with a pre-trained transformer model and build the training data with simulated human feedback using algorithm described in section 3. ## related work post-editing is a pragmatic method that allows human translators to directly correct errors in draft machine translations @xcite . comparing to purely manual translation, it achieves higher productivity while maintaining the human translation quality @xcite . many notable works introduce different levels of human-machine interactions in post-editing. @xcite propose a prefix-based interactive method which enable users to correct the first translation error from left to right in each iteration. @xcite implement a prefix-based interactive translation system and @xcite adopt the prefix constrained translation candidates into a novel input method for translators. @xcite further extend this idea to neural machine translation. the prefix-based protocol is inflexible since users have to follow the left-to-right order. to overcome the weakness of prefix-based approach, @xcite ; @xcite introduce interaction methods that allow users to correct errors at arbitrary position in a machine hypothesis, while @xcite also preventing repeat mistakes by memorizing revision actions. @xcite propose grid beam search to incorporate lexical constraints like words and phrases provided by human translators and force the constraints to appear in hypothesis. recently, some researchers resort to more flexible interactions, which only require mouse click or touch actions. for example, @xcite ; @xcite propose interactive translation methods which ask user to select correct or incorrect segments of a translation with mouse only. similar to our work, @xcite propose a mouse based interactive method which allows users to simply mark the incorrect words in draft machine hypotheses and expect the system to generate refined translations. @xcite propose a multi-modal interface for post-editors which takes pen, touch, and speech modalities into consideration. the protocol that given an initial translation to generate a refined translation, is also used in polishing mechanism in machine translation @xcite and automatic post-editing (ape) task @xcite . the idea of multi-source encoder is also widely used in the field of ape research @xcite . in human-machine interaction scenarios, the human feedback is used as extra information in polishing process. ## conclusion in this paper, we propose touch editing, a flexible and effective interaction approach which allows human translators to revise machine translation results via touch actions. the actions we introduce can be provided with gestures like tapping, panning, swiping or long pressing on touch screens to represent human editing intentions. we present a simulated action extraction method for constructing training data and a dual-encoder model to handle the actions to generate refined translations. we prove the effectiveness of the proposed interaction approach and discuss the applicable scenarios with a free-use study. for future works, we plan to conduct large scale real world experiments to evaluate the productivity of different interactive machine translation methods.