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Register a new userSelf-Supervised Quality Estimation for Machine Translation
تقدير الجودة الخاضعة للإشراف الذاتي للترجمة الآلية
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تهدف تقدير الجودة (QE) من الترجمة الآلية (MT) إلى تقييم جودة الجمل التي ترجمتها الجهاز دون مراجع وهي مهمة في التطبيقات العملية ل MT.تتطلب Training Models QE بيانات موازية ضخمة بأشرفة توضيحية ذات جودة يدوية، وهي تستغرق وقتا طويلا ومكثفة العمالة للحصول عليها.لمعالجة مسألة عدم وجود بيانات تدريب مشروح، تحاول الدراسات السابقة تطوير أساليب QE غير المدعومة.ومع ذلك، يمكن تطبيق عدد قليل جدا منهم على مهام QE على مستوى الجملة والطريق، وقد تعاني من الضوضاء في البيانات الاصطناعية.لتقليل الآثار السلبية للضوضاء، نقترح طريقة للإشراف ذاتي لكل من QE من كل من QE على مستوى الكلمة والطريق، والتي تنفذ تقدير الجودة من خلال استعادة الكلمات المستهدفة الملثمين.تظهر النتائج التجريبية أن أسلوبنا تتفوق على الطرق السابقة غير الخاضعة للرقابة في العديد من مهام QE في أزواج ومجال بلغات مختلفة.
Quality estimation (QE) of machine translation (MT) aims to evaluate the quality of machine-translated sentences without references and is important in practical applications of MT. Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain. To address the issue of the absence of annotated training data, previous studies attempt to develop unsupervised QE methods. However, very few of them can be applied to both sentence- and word-level QE tasks, and they may suffer from noises in the synthetic data. To reduce the negative impact of noises, we propose a self-supervised method for both sentence- and word-level QE, which performs quality estimation by recovering the masked target words. Experimental results show that our method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains.
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Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT). Traditionally, a QE system accepts the original source text and translation from a black-box MT system as input. Recently, a few studies indicate that as a b
y-product of translation, QE benefits from the model and training data's information of the MT system where the translations come from, and it is called the glass-box QE''. In this paper, we extend the definition of glass-box QE'' generally to uncertainty quantification with both black-box'' and glass-box'' approaches and design several features deduced from them to blaze a new trial in improving QE's performance. We propose a framework to fuse the feature engineering of uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. Experiment results show that our method achieves state-of-the-art performances on the datasets of WMT 2020 QE shared task.
Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. Most topic models rely on word co-occurrence for computing a topic, i.e., a weighted set of words that together represent a high-level s
emantic concept. In this paper, we propose a new light-weight Self-Supervised Neural Topic Model (SNTM) that learns a rich context by learning a topic representation jointly from three co-occurring words and a document that the triple originates from. Our experimental results indicate that our proposed neural topic model, SNTM, outperforms previously existing topic models in coherence metrics as well as document clustering accuracy. Moreover, apart from the topic coherence and clustering performance, the proposed neural topic model has a number of advantages, namely, being computationally efficient and easy to train.
An intelligent dialogue system in a multi-turn setting should not only generate the responses which are of good quality, but it should also generate the responses which can lead to long-term success of the dialogue. Although, the current approaches i
mproved the response quality, but they over-look the training signals present in the dialogue data. We can leverage these signals to generate the weakly supervised training data for learning dialog policy and reward estimator, and make the policy take actions (generates responses) which can foresee the future direction for a successful (rewarding) conversation. We simulate the dialogue between an agent and a user (modelled similar to an agent with supervised learning objective) to interact with each other. The agent uses dynamic blocking to generate ranked diverse responses and exploration-exploitation to select among the Top-K responses. Each simulated state-action pair is evaluated (works as a weak annotation) with three quality modules: Semantic Relevant, Semantic Coherence and Consistent Flow. Empirical studies with two benchmarks indicate that our model can significantly out-perform the response quality and lead to a successful conversation on both automatic evaluation and human judgment.
Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks. However, in real-world scenarios, massive monolingual corpora do not exist for some extreme
ly low-resource languages such as Estonian, and UNMT systems usually perform poorly when there is not adequate training corpus for one language. In this paper, we first define and analyze the unbalanced training data scenario for UNMT. Based on this scenario, we propose UNMT self-training mechanisms to train a robust UNMT system and improve its performance in this case. Experimental results on several language pairs show that the proposed methods substantially outperform conventional UNMT systems.
This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with noisy input for robust neural machine translation by introducing self-correcting predictors. Different from previous robust approaches, Secoco enables NMT
to explicitly correct noisy inputs and delete specific errors simultaneously with the translation decoding process. Secoco is able to achieve significant improvements over strong baselines on two real-world test sets and a benchmark WMT dataset with good interpretability. We will make our code and dataset publicly available soon.
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