Machine Translation Quality Estimation Research Articles

Bibliometric analysis and visualization of translation assessment: Research theme, evolution and hotspots

Translation assessment, referring to evaluating various aspects related to translation, is crucial to the improvement of translation competence and quality. This study, taking valid papers on translation assessment published in WoS core collection from 2000 to 2022 as research samples, visualizes and reviews the research theme, research evolutions and emerging hot topics of translation assessment through bibliometric analysis. The number of annual publications shows that despite the general growth, the number of articles on translation assessment fluctuates from year to year. By Cite Space-based analysis of keywords co-occurrence, keyword clustering, time zone and burst detection, it finds that translation assessment mainly covers five themes, namely translation competence, translation quality, machine translation, translation teaching and training, and others. The overall evolution has been a gradual shift from concepts, metrics and evaluations of topics related to translation assessment into a topic of in-depth and interdisciplinary study. The hotspots in recent years include translation competence acquisition, neural machine translation and translation quality estimation. The findings reveal research evolutions and hot spots, propose implications for further translation assessment research and provide references for scholars who are interested in this topic.

Multi-view fusion for universal translation quality estimation

Machine translation quality estimation (QE) aims to evaluate the result of translation without reference. Despite the progress it has made, state-of-the-art QE models are proven to be biased. More specifically, they over-rely on spurious statistical features while ignoring the bilingual semantic adequacy, leading to performance degradation. Besides, existing approaches require large amounts of annotation data, restricting their applications in new domains and languages. In this work, we propose a universal framework for quality estimation based on multi-view fusion. We first introduce noise to the target side of the parallel sentence pair, either by pre-trained language model or by large language model. After that, with the clean parallel pairs and the noised pairs as different views, the QE model is trained to distinguish the clean pairs from the noised ones. Our method can improve the accuracy and generalizability in supervised scenario, and can solely perform estimation in zero-shot scenario. We perform experiments on WMT QE evaluation datasets under different scenarios, verifying the effectiveness of our method. We also make an in-depth investigation of the bias of QE model.

Denoising Pre-training for Machine Translation Quality Estimation with Curriculum Learning

Quality estimation (QE) aims to assess the quality of machine translations when reference translations are unavailable. QE plays a crucial role in many real-world applications of machine translation. Because labeled QE data are usually limited in scale, recent research, such as DirectQE, pre-trains QE models with pseudo QE data and obtains remarkable performance. However, there tends to be inevitable noise in the pseudo data, hindering models from learning QE accurately. Our study shows that the noise mainly comes from the differences between pseudo and real translation outputs. To handle this problem, we propose CLQE, a denoising pre-training framework for QE based on curriculum learning. More specifically, we propose to measure the degree of noise in the pseudo QE data with some metrics based on statistical or distributional features. With the guidance of these metrics, CLQE gradually pre-trains the QE model using data from cleaner to noisier. Experiments on various benchmarks reveal that CLQE outperforms DirectQE and other strong baselines. We also show that with our framework, pre-training converges faster than directly using the pseudo data. We make our CLQE code available (https://github.com/NJUNLP/njuqe).

The Role of Machine Translation Quality Estimation in the Post-Editing Workflow

As Machine Translation (MT) becomes increasingly ubiquitous, so does its use in professional translation workflows. However, its proliferation in the translation industry has brought about new challenges in the field of Post-Editing (PE). We are now faced with a need to find effective tools to assess the quality of MT systems to avoid underpayments and mistrust by professional translators. In this scenario, one promising field of study is MT Quality Estimation (MTQE), as this aims to determine the quality of an automatic translation and, indirectly, its degree of post-editing difficulty. However, its impact on the translation workflows and the translators’ cognitive load is still to be fully explored. We report on the results of an impact study engaging professional translators in PE tasks using MTQE. To assess the translators’ cognitive load we measure their productivity both in terms of time and effort (keystrokes) in three different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that good MTQE information can improve post-editing efficiency and decrease the cognitive load on translators. This is especially true for cases with low MT quality.

An Oblivious Approach to Machine Translation Quality Estimation

Machine translation (MT) is being used by millions of people daily, and therefore evaluating the quality of such systems is an important task. While human expert evaluation of MT output remains the most accurate method, it is not scalable by any means. Automatic procedures that perform the task of Machine Translation Quality Estimation (MT-QE) are typically trained on a large corpus of source–target sentence pairs, which are labeled with human judgment scores. Furthermore, the test set is typically drawn from the same distribution as the train. However, recently, interest in low-resource and unsupervised MT-QE has gained momentum. In this paper, we define and study a further restriction of the unsupervised MT-QE setting that we call oblivious MT-QE. Besides having no access no human judgment scores, the algorithm has no access to the test text’s distribution. We propose an oblivious MT-QE system based on a new notion of sentence cohesiveness that we introduce. We tested our system on standard competition datasets for various language pairs. In all cases, the performance of our system was comparable to the performance of the non-oblivious baseline system provided by the competition organizers. Our results suggest that reasonable MT-QE can be carried out even in the restrictive oblivious setting.

DirectQE: Direct Pretraining for Machine Translation Quality Estimation

Machine Translation Quality Estimation (QE) is a task of predicting the quality of machine translations without relying on any reference. Recently, the predictor-estimator framework trains the predictor as a feature extractor, which leverages the extra parallel corpora without QE labels, achieving promising QE performance. However, we argue that there are gaps between the predictor and the estimator in both data quality and training objectives, which preclude QE models from benefiting from a large number of parallel corpora more directly. We propose a novel framework called DirectQE that provides a direct pretraining for QE tasks. In DirectQE, a generator is trained to produce pseudo data that is closer to the real QE data, and a detector is pretrained on these data with novel objectives that are akin to the QE task. Experiments on widely used benchmarks show that DirectQE outperforms existing methods, without using any pretraining models such as BERT. We also give extensive analyses showing how fixing the two gaps contributes to our improvements.

Assessing Human Post-Editing Efforts to Compare the Performance of Three Machine Translation Engines for English to Russian Translation of Cochrane Plain Language Health Information: Results of a Randomised Comparison

Cochrane produces independent research to improve healthcare decisions. It translates its research summaries into different languages to enable wider access, relying largely on volunteers. Machine translation (MT) could facilitate efficiency in Cochrane’s low-resource environment. We compared three off-the-shelf machine translation engines (MTEs)—DeepL, Google Translate and Microsoft Translator—for Russian translations of Cochrane plain language summaries (PLSs) by assessing the quantitative human post-editing effort within an established translation workflow and quality assurance process. 30 PLSs each were pre-translated with one of the three MTEs. Ten volunteer translators post-edited nine randomly assigned PLSs each—three per MTE—in their usual translation system, Memsource. Two editors performed a second editing step. Memsource’s Machine Translation Quality Estimation (MTQE) feature provided an artificial intelligence (AI)-powered estimate of how much editing would be required for each PLS, and the analysis feature calculated the amount of human editing after each editing step. Google Translate performed the best with highest average quality estimates for its initial MT output, and the lowest amount of human post-editing. DeepL performed slightly worse, and Microsoft Translator worst. Future developments in MT research and the associated industry may change our results.

Analysis and evaluation on the quality of news text machine translation based on neural network

Machine translation quality estimation is an important task in natural language processing. Unlike traditional automatic machine translation evaluation methods, the quality of machine translation is evaluated by the translation quality estimation method without using manual reference translation. In view of the fact that the feature extraction of current sentence-level machine translation quality estimation relies heavily on linguistic analysis, which leads to insufficient generalization ability and restricts the performance of subsequent support vector regression algorithm, it is proposed to extract the features of sentence vectors by using contextual word prediction model and matrix decomposition model in deep learning, and combine them with the features of recurrent neural network language model to improve the correlation between automatic estimation of translation quality and manual evaluation. The experimental results on WMT15 and WMT16 translation quality estimation subtask data sets show that the performance statistics of the method of extracting sentence vector features by adopting context word prediction model are consistently superior to that of the traditional Quest method and continuous space language model sentence vector feature extraction method. It reveals that the proposed feature extraction method not only requires no linguistic analysis, but also significantly improves the effect of translation quality estimation.

Predicting insertion positions in word-level machine translation quality estimation

Word-level machine translation (MT) quality estimation (QE) is usually formulated as the task of automatically identifying which words need to be edited (either deleted or replaced) in a translation T produced by an MT system. The advantage of estimating MT quality at the word level is that this information can be used to guide post-editors since it enables the identification of the specific words in T that need to be edited in order to ease their work. However, word-level MT QE, as defined in the current literature, has an obvious limitation: it does not identify the positions in T in which missing words need to be inserted. To deal with this limitation, we propose a method which identifies both word deletions and insertion positions in T. This is, to the best of our knowledge, the first approach allowing the identification of insertion positions in word-level MT QE. The method proposed can use any source of bilingual information – such as MT, dictionaries, or phrase-level translation memories – to extract features that are then used by a neural network to produce a prediction for both words and insertion positions (gaps between words) in the translation T. In this paper, several feature sets and neural network architectures are explored and evaluated on publicly-available datasets used in previous evaluation campaigns for word-level MT QE. The results confirm the feasibility of the proposed approach, as well as the usefulness of sharing information between the two prediction tasks in order to obtain more reliable quality estimations.

QE::GUI – A Graphical User Interface for Quality Estimation

Abstract Despite its wide applicability, Quality Estimation (QE) of Machine Translation (MT) poses a difficult entry barrier since there are no open source tools with a graphical user interface (GUI). Here we present a tool in this direction by connecting the back-end of the QE decision-making mechanism with a web-based GUI. The interface allows the user to post requests to the QE engine and get a visual response with the results. Additionally we provide pre-trained QE models for easier launching of the app. The tool is written in Python so that it can leverage the rich natural language processing capabilities of the popular dynamic programming language, which is at the same time supported by top web-server environments.

Identifying the Machine Translation Error Types with the Greatest Impact on Post-editing Effort.

Translation Environment Tools make translators’ work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from scratch, and determine whether or not to provide translators with machine translation output. Current machine translation quality estimation systems heavily rely on automatic metrics, even though they do not accurately capture actual post-editing effort. In addition, these systems do not take translator experience into account, even though novices’ translation processes are different from those of professional translators. In this paper, we report on the impact of machine translation errors on various types of post-editing effort indicators, for professional translators as well as student translators. We compare the impact of MT quality on a product effort indicator (HTER) with that on various process effort indicators. The translation and post-editing process of student translators and professional translators was logged with a combination of keystroke logging and eye-tracking, and the MT output was analyzed with a fine-grained translation quality assessment approach. We find that most post-editing effort indicators (product as well as process) are influenced by machine translation quality, but that different error types affect different post-editing effort indicators, confirming that a more fine-grained MT quality analysis is needed to correctly estimate actual post-editing effort. Coherence, meaning shifts, and structural issues are shown to be good indicators of post-editing effort. The additional impact of experience on these interactions between MT quality and post-editing effort is smaller than expected.

Questing for Quality Estimation A User Study

AbstractPost-Editing of Machine Translation (MT) has become a reality in professional translation workflows. In order to optimize the management of projects that use post-editing and avoid underpayments and mistrust from professional translators, effective tools to assess the quality of Machine Translation (MT) systems need to be put in place. One field of study that could address this problem is Machine Translation Quality Estimation (MTQE), which aims to determine the quality of MT without an existing reference. Accurate and reliable MTQE can help project managers and translators alike, as it would allow estimating more precisely the cost of post-editing projects in terms of time and adequate fares by discarding those segments that are not worth post-editing (PE) and have to be translated from scratch.In this paper, we report on the results of an impact study which engages professional translators in PE tasks using MTQE. We measured translators’ productivity in different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that QE information, when accurate, improves post-editing efficiency.

Towards Optimizing MT for Post-Editing Effort: Can BLEU Still Be Useful?

AbstractWe propose a simple, linear-combination automatic evaluation measure (AEM) to approximate post-editing (PE) effort. Effort is measured both as PE time and as the number of PE operations performed. The ultimate goal is to define an AEM that can be used to optimize machine translation (MT) systems to minimize PE effort, but without having to perform unfeasible repeated PE during optimization. As PE effort is expected to be an extensive magnitude (i.e., one growing linearly with the sentence length and which may be simply added to represent the effort for a set of sentences), we use a linear combination of extensive and pseudo-extensive features. One such pseudo-extensive feature, 1–BLEU times the length of the reference, proves to be almost as good a predictor of PE effort as the best combination of extensive features. Surprisingly, effort predictors computed using independently obtained reference translations perform reasonably close to those using actual post-edited references. In the early stage of this research and given the inherent complexity of carrying out experiments with professional post-editors, we decided to carry out an automatic evaluation of the AEMs proposed rather than a manual evaluation to measure the effort needed to post-edit the output of an MT system tuned on these AEMs. The results obtained seem to support current tuning practice using BLEU, yet pointing at some limitations. Apart from this intrinsic evaluation, an extrinsic evaluation was also carried out in which the AEMs proposed were used to build synthetic training corpora for MT quality estimation, with results comparable to those obtained when training with measured PE efforts.

A Bayesian non-linear method for feature selection in machine translation quality estimation

We perform a systematic analysis of the effectiveness of features for the problem of predicting the quality of machine translation (MT) at the sentence level. Starting from a comprehensive feature set, we apply a technique based on Gaussian processes, a Bayesian non-linear learning method, to automatically identify features leading to accurate model performance. We consider application to several datasets across different language pairs and text domains, with translations produced by various MT systems and scored for quality according to different evaluation criteria. We show that selecting features with this technique leads to significantly better performance in most datasets, as compared to using the complete feature sets or a state-of-the-art feature selection approach. In addition, we identify a small set of features which seem to perform well across most datasets.

RankEval: Open Tool for Evaluation of Machine-Learned Ranking

Abstract Recent research and applications for evaluation and quality estimation of Machine Translation require statistical measures for comparing machine-predicted ranking against gold sets annotated by humans. Additional to the existing practice of measuring segment-level correlation with Kendall tau, we propose using ranking metrics from the research field of Information Retrieval such as Mean Reciprocal Rank, Normalized Discounted Cumulative Gain and Expected Reciprocal Rank. These reward systems that predict correctly the highest ranked items than the one of lower ones. We present an open source tool ”RANKEVAL” providing implementation of these metrics. It can be either run independently as a script supporting common formats or can be imported to any Python application.

Dimensionality reduction methods for machine translation quality estimation

Quality estimation (QE) for machine translation is usually addressed as a regression problem where a learning model is used to predict a quality score from a (usually highly-redundant) set of features that represent the translation. This redundancy hinders model learning, and thus penalizes the performance of quality estimation systems. We propose different dimensionality reduction methods based on partial least squares regression to overcome this problem, and compare them against several reduction methods previously used in the QE literature. Moreover, we study how the use of such methods influence the performance of different learning models. Experiments carried out on the English-Spanish WMT12 QE task showed that it is possible to improve prediction accuracy while significantly reducing the size of the feature sets.

QuEst – Design, Implementation and Extensions of a Framework for Machine Translation Quality Estimation

In t his paper we present QE, an open source framework for machine translation quality estimation. The framework includes a feature extraction component and a machine learning component. We describe the architecture of the system and its use, focusing on the feature extraction component and on how to add new feature extractors. We also include experiments with features and learning algorithms available in the framework using the dataset of the WMT13 Quality Estimation shared task.