pwc-archive/papers-with-abstracts huggingface dataset:
NJU Submissions for the WMT19 Quality Estimation Shared Task
Authors: Hou Qi
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Sentence, Translation

In this paper, we describe the submissions of the team from Nanjing University for the WMT19 sentence-level Quality Estimation (QE) shared task on English-German language pair. We develop two approaches based on a two-stage neural QE model consisting of a feature extractor and a quality estimator. More specifically, one of the proposed approaches employs the translation knowledge between the two languages from two different translation directions; while the other one employs extra monolingual knowledge from both source and target sides, obtained by pre-training deep self-attention networks. To efficiently train these two-stage models, a joint learning training method is applied. Experiments show that the ensemble model of the above two models achieves the best results on the benchmark dataset of the WMT17 sentence-level QE shared task and obtains competitive results in WMT19, ranking 3rd out of 10 submissions.
Quality Estimation and Translation Metrics via Pre-trained Word and Sentence Embeddings
Authors: Elizaveta Yankovskaya, Andre T{\"a}ttar, Mark Fishel
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, regression, Sentence, Sentence Embeddings, Translation

We propose the use of pre-trained embeddings as features of a regression model for sentence-level quality estimation of machine translation. In our work we combine freely available BERT and LASER multilingual embeddings to train a neural-based regression model. In the second proposed method we use as an input features not only pre-trained embeddings, but also log probability of any machine translation (MT) system. Both methods are applied to several language pairs and are evaluated both as a classical quality estimation system (predicting the HTER score) as well as an MT metric (predicting human judgements of translation quality).
SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation
Authors: Junpei Zhou, Zhisong Zhang, Zecong Hu
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, NMT, Sentence, Translation

Quality estimation (QE) of machine translation (MT) systems is a task of growing importance. It reduces the cost of post-editing, allowing machine-translated text to be used in formal occasions. In this work, we describe our submission system in WMT 2019 sentence-level QE task. We mainly explore the utilization of pre-trained translation models in QE and adopt a bi-directional translation-like strategy. The strategy is similar to ELMo, but additionally conditions on source sentences. Experiments on WMT QE dataset show that our strategy, which makes the pre-training slightly harder, can bring improvements for QE. In WMT-2019 QE task, our system ranked in the second place on En-De NMT dataset and the third place on En-Ru NMT dataset.
Transformer-based Automatic Post-Editing Model with Joint Encoder and Multi-source Attention of Decoder
Authors: WonKee Lee, Jaehun Shin, Jong-Hyeok Lee
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Automatic Post-Editing, Decoder, Translation

This paper describes POSTECH{'}s submission to the WMT 2019 shared task on Automatic Post-Editing (APE). In this paper, we propose a new multi-source APE model by extending Transformer. The main contributions of our study are that we 1) reconstruct the encoder to generate a joint representation of translation (mt) and its src context, in addition to the conventional src encoding and 2) suggest two types of multi-source attention layers to compute attention between two outputs of the encoder and the decoder state in the decoder. Furthermore, we train our model by applying various teacher-forcing ratios to alleviate exposure bias. Finally, we adopt the ensemble technique across variations of our model. Experiments on the WMT19 English-German APE data set show improvements in terms of both TER and BLEU scores over the baseline. Our primary submission achieves -0.73 in TER and +1.49 in BLEU compare to the baseline.
USAAR-DFKI -- The Transference Architecture for English--German Automatic Post-Editing
Authors: Santanu Pal, Hongfei Xu, Nico Herbig, Antonio Kr{\"u}ger, Josef van Genabith
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Automatic Post-Editing, Decoder, Machine Translation, Translation

In this paper we present an English{--}German Automatic Post-Editing (APE) system called transference, submitted to the APE Task organized at WMT 2019. Our transference model is based on a multi-encoder transformer architecture. Unlike previous approaches, it (i) uses a transformer encoder block for src, (ii) followed by a transformer decoder block, but without masking, for self-attention on mt, which effectively acts as second encoder combining src {--}{\textgreater} mt, and (iii) feeds this representation into a final decoder block generating pe. Our model improves over the raw black-box neural machine translation system by 0.9 and 1.0 absolute BLEU points on the WMT 2019 APE development and test set. Our submission ranked 3rd, however compared to the two top systems, performance differences are not statistically significant.
APE through Neural and Statistical MT with Augmented Data. ADAPT/DCU Submission to the WMT 2019 APE Shared Task
Authors: Dimitar Shterionov, Joachim Wagner, F{\'e}lix do Carmo
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Automatic Post-Editing, Domain Adaptation, Machine Translation, Translation

Automatic post-editing (APE) can be reduced to a machine translation (MT) task, where the source is the output of a specific MT system and the target is its post-edited variant. However, this approach does not consider context information that can be found in the original source of the MT system. Thus a better approach is to employ multi-source MT, where two input sequences are considered {--} the one being the original source and the other being the MT output. Extra context information can be introduced in the form of extra tokens that identify certain global property of a group of segments, added as a prefix or a suffix to each segment. Successfully applied in domain adaptation of MT as well as on APE, this technique deserves further attention. In this work we investigate multi-source neural APE (or NPE) systems with training data which has been augmented with two types of extra context tokens. We experiment with authentic and synthetic data provided by WMT 2019 and submit our results to the APE shared task. We also experiment with using statistical machine translation (SMT) methods for APE. While our systems score bellow the baseline, we consider this work a step towards understanding the added value of extra context in the case of APE.
Effort-Aware Neural Automatic Post-Editing
Authors: Amirhossein Tebbifakhr, Matteo Negri, Marco Turchi
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Automatic Post-Editing, NMT, Sentence

For this round of the WMT 2019 APE shared task, our submission focuses on addressing the {``}over-correction{''} problem in APE. Over-correction occurs when the APE system tends to rephrase an already correct MT output, and the resulting sentence is penalized by a reference-based evaluation against human post-edits. Our intuition is that this problem can be prevented by informing the system about the predicted quality of the MT output or, in other terms, the expected amount of needed corrections. For this purpose, following the common approach in multilingual NMT, we prepend a special token to the beginning of both the source text and the MT output indicating the required amount of post-editing. Following the best submissions to the WMT 2018 APE shared task, our backbone architecture is based on multi-source Transformer to encode both the MT output and the corresponding source text. We participated both in the English-German and English-Russian subtasks. In the first subtask, our best submission improved the original MT output quality up to +0.98 BLEU and -0.47 TER. In the second subtask, where the higher quality of the MT output increases the risk of over-correction, none of our submitted runs was able to improve the MT output.
Terminology-Aware Segmentation and Domain Feature for the WMT19 Biomedical Translation Task
Authors: Casimiro Pio Carrino, Bardia Rafieian, Marta R. Costa-juss{\`a}, Jos{\'e} A. R. Fonollosa
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: NMT, Translation

In this work, we give a description of the TALP-UPC systems submitted for the WMT19 Biomedical Translation Task. Our proposed strategy is NMT model-independent and relies only on one ingredient, a biomedical terminology list. We first extracted such a terminology list by labelling biomedical words in our training dataset using the BabelNet API. Then, we designed a data preparation strategy to insert the terms information at a token level. Finally, we trained the Transformer model with this terms-informed data. Our best-submitted system ranked 2nd and 3rd for Spanish-English and English-Spanish translation directions, respectively.
Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation
Authors: Noor-e- Hira, Sadaf Abdul Rauf, Kiran Kiani, Ammara Zafar, Raheel Nawaz
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Information Retrieval, Machine Translation, Retrieval, Transfer Learning, Translation

Transfer Learning and Selective data training are two of the many approaches being extensively investigated to improve the quality of Neural Machine Translation systems. This paper presents a series of experiments by applying transfer learning and selective data training for participation in the Bio-medical shared task of WMT19. We have used Information Retrieval to selectively choose related sentences from out-of-domain data and used them as additional training data using transfer learning. We also report the effect of tokenization on translation model performance.
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task
Authors: Wei Peng, Jianfeng Liu, Liangyou Li, Qun Liu
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Domain Adaptation, Machine Translation, NMT, Transfer Learning, Translation

This paper describes Huawei{'}s neural machine translation systems for the WMT 2019 biomedical translation shared task. We trained and fine-tuned our systems on a combination of out-of-domain and in-domain parallel corpora for six translation directions covering English{--}Chinese, English{--}French and English{--}German language pairs. Our submitted systems achieve the best BLEU scores on English{--}French and English{--}German language pairs according to the official evaluation results. In the English{--}Chinese translation task, our systems are in the second place. The enhanced performance is attributed to more in-domain training and more sophisticated models developed. Development of translation models and transfer learning (or domain adaptation) methods has significantly contributed to the progress of the task.
BSC Participation in the WMT Translation of Biomedical Abstracts
Authors: Felipe Soares, Martin Krallinger
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

This paper describes the machine translation systems developed by the Barcelona Supercomputing (BSC) team for the biomedical translation shared task of WMT19. Our system is based on Neural Machine Translation unsing the OpenNMT-py toolkit and Transformer architecture. We participated in four translation directions for the English/Spanish and English/Portuguese language pairs. To create our training data, we concatenated several parallel corpora, both from in-domain and out-of-domain sources, as well as terminological resources from UMLS.
The MLLP-UPV Spanish-Portuguese and Portuguese-Spanish Machine Translation Systems for WMT19 Similar Language Translation Task
Authors: Pau Baquero-Arnal, Javier Iranzo-S{\'a}nchez, Jorge Civera, Alfons Juan
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Domain Adaptation, Machine Translation, Translation

This paper describes the participation of the MLLP research group of the Universitat Polit{\`e}cnica de Val{\`e}ncia in the WMT 2019 Similar Language Translation Shared Task. We have submitted systems for the Portuguese â†” Spanish language pair, in both directions. We have submitted systems based on the Transformer architecture as well as an in development novel architecture which we have called 2D alternating RNN. We have carried out domain adaptation through fine-tuning.
Machine Translation from an Intercomprehension Perspective
Authors: Yu Chen, Tania Avgustinova
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

Within the first shared task on machine translation between similar languages, we present our first attempts on Czech to Polish machine translation from an intercomprehension perspective. We propose methods based on the mutual intelligibility of the two languages, taking advantage of their orthographic and phonological similarity, in the hope to improve over our baselines. The translation results are evaluated using BLEU. On this metric, none of our proposals could outperform the baselines on the final test set. The current setups are rather preliminary, and there are several potential improvements we can try in the future.
Utilizing Monolingual Data in NMT for Similar Languages: Submission to Similar Language Translation Task
Authors: Jyotsana Khatri, Pushpak Bhattacharyya
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, NMT, Translation

This paper describes our submission to Shared Task on Similar Language Translation in Fourth Conference on Machine Translation (WMT 2019). We submitted three systems for Hindi -{\textgreater} Nepali direction in which we have examined the performance of a RNN based NMT system, a semi-supervised NMT system where monolingual data of both languages is utilized using the architecture by and a system trained with extra synthetic sentences generated using copy of source and target sentences without using any additional monolingual data.
Neural Machine Translation: Hindi-Nepali
Authors: Sahinur Rahman Laskar, Partha Pakray, B, Sivaji yopadhyay
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, NMT, Translation

With the extensive use of Machine Translation (MT) technology, there is progressively interest in directly translating between pairs of similar languages. Because the main challenge is to overcome the limitation of available parallel data to produce a precise MT output. Current work relies on the Neural Machine Translation (NMT) with attention mechanism for the similar language translation of WMT19 shared task in the context of Hindi-Nepali pair. The NMT systems trained the Hindi-Nepali parallel corpus and tested, analyzed in Hindi â‡” Nepali translation. The official result declared at WMT19 shared task, which shows that our NMT system obtained Bilingual Evaluation Understudy (BLEU) score 24.6 for primary configuration in Nepali to Hindi translation. Also, we have achieved BLEU score 53.7 (Hindi to Nepali) and 49.1 (Nepali to Hindi) in contrastive system type.
NICT's Machine Translation Systems for the WMT19 Similar Language Translation Task
Authors: Benjamin Marie, Raj Dabre, Atsushi Fujita
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, NMT, Translation

This paper presents the NICT{'}s participation in the WMT19 shared Similar Language Translation Task. We participated in the Spanish-Portuguese task. For both translation directions, we prepared state-of-the-art statistical (SMT) and neural (NMT) machine translation systems. Our NMT systems with the Transformer architecture were trained on the provided parallel data enlarged with a large quantity of back-translated monolingual data. Our primary submission to the task is the result of a simple combination of our SMT and NMT systems. According to BLEU, our systems were ranked second and third respectively for the Portuguese-to-Spanish and Spanish-to-Portuguese translation directions. For contrastive experiments, we also submitted outputs generated with an unsupervised SMT system.
Panlingua-KMI MT System for Similar Language Translation Task at WMT 2019
Authors: Atul Kr. Ojha, Ritesh Kumar, Akanksha Bansal, Priya Rani
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, NMT, Translation

The present paper enumerates the development of Panlingua-KMI Machine Translation (MT) systems for Hindi ↔ Nepali language pair, designed as part of the Similar Language Translation Task at the WMT 2019 Shared Task. The Panlingua-KMI team conducted a series of experiments to explore both the phrase-based statistical (PBSMT) and neural methods (NMT). Among the 11 MT systems prepared under this task, 6 PBSMT systems were prepared for Nepali-Hindi, 1 PBSMT for Hindi-Nepali and 2 NMT systems were developed for Nepali↔Hindi. The results show that PBSMT could be an effective method for developing MT systems for closely-related languages. Our Hindi-Nepali PBSMT system was ranked 2nd among the 13 systems submitted for the pair and our Nepali-Hindi PBSMTsystem was ranked 4th among the 12 systems submitted for the task.
UDS--DFKI Submission to the WMT2019 Czech--Polish Similar Language Translation Shared Task
Authors: Santanu Pal, Marcos Zampieri, Josef van Genabith
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Translation

In this paper we present the UDS-DFKI system submitted to the Similar Language Translation shared task at WMT 2019. The first edition of this shared task featured data from three pairs of similar languages: Czech and Polish, Hindi and Nepali, and Portuguese and Spanish. Participants could choose to participate in any of these three tracks and submit system outputs in any translation direction. We report the results obtained by our system in translating from Czech to Polish and comment on the impact of out-of-domain test data in the performance of our system. UDS-DFKI achieved competitive performance ranking second among ten teams in Czech to Polish translation.
Neural Machine Translation of Low-Resource and Similar Languages with Backtranslation
Authors: Michael Przystupa, Muhammad Abdul-Mageed
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

We present our contribution to the WMT19 Similar Language Translation shared task. We investigate the utility of neural machine translation on three low-resource, similar language pairs: Spanish {--} Portuguese, Czech {--} Polish, and Hindi {--} Nepali. Since state-of-the-art neural machine translation systems still require large amounts of bitext, which we do not have for the pairs we consider, we focus primarily on incorporating monolingual data into our models with backtranslation. In our analysis, we found Transformer models to work best on Spanish {--} Portuguese and Czech {--} Polish translation, whereas LSTMs with global attention worked best on Hindi {--} Nepali translation.
The University of Helsinki Submissions to the WMT19 Similar Language Translation Task
Authors: Yves Scherrer, Ra{\'u}l V{\'a}zquez, Sami Virpioja
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Segmentation, Translation

This paper describes the University of Helsinki Language Technology group{'}s participation in the WMT 2019 similar language translation task. We trained neural machine translation models for the language pairs Czech {\textless}-{\textgreater} Polish and Spanish {\textless}-{\textgreater} Portuguese. Our experiments focused on different subword segmentation methods, and in particular on the comparison of a cognate-aware segmentation method, Cognate Morfessor, with character segmentation and unsupervised segmentation methods for which the data from different languages were simply concatenated. We did not observe major benefits from cognate-aware segmentation methods, but further research may be needed to explore larger parts of the parameter space. Character-level models proved to be competitive for translation between Spanish and Portuguese, but they are slower in training and decoding.
Dual Monolingual Cross-Entropy Delta Filtering of Noisy Parallel Data
Authors: Amittai Axelrod, Anish Kumar, Steve Sloto
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

We introduce a purely monolingual approach to filtering for parallel data from a noisy corpus in a low-resource scenario. Our work is inspired by Junczysdowmunt:2018, but we relax the requirements to allow for cases where no parallel data is available. Our primary contribution is a dual monolingual cross-entropy delta criterion modified from Cynical data selection Axelrod:2017, and is competitive (within 1.8 BLEU) with the best bilingual filtering method when used to train SMT systems. Our approach is featherweight, and runs end-to-end on a standard laptop in three hours.
NRC Parallel Corpus Filtering System for WMT 2019
Authors: Gabriel Bernier-Colborne, Chi-kiu Lo
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

We describe the National Research Council Canada team{'}s submissions to the parallel corpus filtering task at the Fourth Conference on Machine Translation.
Quality and Coverage: The AFRL Submission to the WMT19 Parallel Corpus Filtering for Low-Resource Conditions Task
Authors: Grant Erdmann, Jeremy Gwinnup
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

The WMT19 Parallel Corpus Filtering For Low-Resource Conditions Task aims to test various methods of filtering a noisy parallel corpora, to make them useful for training machine translation systems. This year the noisy corpora are the relatively low-resource language pairs of Nepali-English and Sinhala-English. This papers describes the Air Force Research Laboratory (AFRL) submissions, including preprocessing methods and scoring metrics. Numerical results indicate a benefit over baseline and the relative benefits of different options.
Webinterpret Submission to the WMT2019 Shared Task on Parallel Corpus Filtering
Authors: Jes{\'u}s Gonz{\'a}lez-Rubio
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

This document describes the participation of Webinterpret in the shared task on parallel corpus filtering at the Fourth Conference on Machine Translation (WMT 2019). Here, we describe the main characteristics of our approach and discuss the results obtained on the data sets published for the shared task.
Noisy Parallel Corpus Filtering through Projected Word Embeddings
Authors: Murathan Kurfal{\i}, Robert {\"O}stling
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation, Word Embeddings

We present a very simple method for parallel text cleaning of low-resource languages, based on projection of word embeddings trained on large monolingual corpora in high-resource languages. In spite of its simplicity, we approach the strong baseline system in the downstream machine translation evaluation.