Dominican Republic

This paper presents the JHU-Microsoft joint submission for WMT 2021 quality estimation shared task. We only participate in Task 2 (post-editing effort estimation) of the shared task, focusing on the target-side word-level quality estimation. The techniques we experimented with include Levenshtein Transformer training and data augmentation with a combination of forward, backward, round-trip translation, and pseudo post-editing of the MT output. We demonstrate the competitiveness of our system compared to the widely adopted OpenKiwi-XLM baseline. Our system is also the top-ranking system on the MT MCC metric for the English-German language pair.

EMNLP 2021

The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task

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Existing approaches for audio-language task- specific prediction focus on building compli- cated late-fusion mechanisms. However, these models face challenges of overfitting with lim- ited labels and poor generalization. In this pa- per, we present a Cross-modal Transformer for Audio-and-Language, i.e., CTAL, which aims to learn the intra- and inter- modali- ties connections between audio and language through two proxy tasks from a large num- ber of audio-and-language pairs: masked language modeling and masked cross-modal acoustic modeling. After fine-tuning our CTAL model on multiple downstream audio- and-language tasks, we observe significant improvements on different tasks, including emotion classification, sentiment analysis, and speaker verification. Furthermore, we design a fusion mechanism in the fine-tuning phase, which allows CTAL to achieve better perfor- mance. Lastly, we conduct detailed ablation studies to demonstrate that both our novel cross-modality fusion component and audio- language pre-training methods contribute to the promising results.

CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations

Reading order detection is the cornerstone to understanding visually-rich documents (e.g., receipts and forms). Unfortunately, no existing work took advantage of advanced deep learning models because it is too laborious to annotate a large enough dataset. We observe that the reading order of WORD documents is embedded in their XML metadata; meanwhile, it is easy to convert WORD documents to PDFs or images. Therefore, in an automated manner, we construct ReadingBank, a benchmark dataset that contains reading order, text, and layout information for 500,000 document images covering a wide spectrum of document types. This first-ever large-scale dataset unleashes the power of deep neural networks for reading order detection. Specifically, our proposed LayoutReader captures the text and layout information for reading order prediction using the seq2seq model. It performs almost perfectly in reading order detection and significantly improves both open-source and commercial OCR engines in ordering text lines in their results in our experiments. The dataset and models are publicly available at https: //aka.ms/layoutreader.

LayoutReader: Pre-training of Text and Layout for Reading Order Detection

Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in an input sentence, which is significantly important in real-world applications. To perform ABSA, one faces a long-standing challenge that ABSA requires the model to have a good understanding of the contextual information, especially the particular patterns that suggests the sentiment polarity. However, these patterns vary in different sentences, especially when the sentences come from different sources (domains). Although leveraging extra labeled data from different sources (domains) is always a promising solution to address the challenge, in practical applications, these labeled data are always stored at different locations and might be inaccessible to each other due to privacy or legal concerns (e.g., the data are owned by different companies). To address the issue and benefit from extra labeled data, we propose a novel ABSA model with federated learning (FL) adopted to overcome the data isolation limitations and topic memory (TM) proposed to enhance FL by accommodating the cases of data from diverse sources (domains). Particularly, TM contributes to overcoming difficulties in identifying domains due to data inaccessibility by providing useful categorical information for localized predictions. Experimental results on a simulated environment for FL with three nodes demonstrate the effectiveness of our approach, where TM-FL outperforms different baselines including some well-designed FL frameworks.

Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories

As an important task in opinion mining, comparative opinion mining aims to identify comparative sentences from product reviews, extract the comparative elements, and obtain the corresponding comparative opinion tuples. However, most previous studies simply regarded comparative tuple extraction as comparative element extraction, but ignored the fact that many comparative sentences may contain multiple comparisons. The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences. To address these limitations, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference). Secondly, based on the existing comparative opinion mining corpora, we make supplementary annotations and construct three datasets for the COQE task. Finally, we benchmark the COQE task by proposing a new BERT-based multi-stage approach as well as three baseline systems extended from previous methods. Experimental results show that the new approach significantly outperforms three baseline systems on three datasets for the COQE task.

Comparative Opinion Quintuple Extraction from Product Reviews

We present the joint contribution of IST and Unbabel to the WMT 2021 Shared Task on Quality Estimation. Our team participated on two tasks: Direct Assessment and Post-Editing Effort, encompassing a total of 35 submissions. For all submissions, our efforts focused on training multilingual models on top of OpenKiwi predictor-estimator architecture, using pre-trained multilingual encoders combined with adapters. We further experiment with and uncertainty-related objectives and features as well as training on out-of-domain direct assessment data.

IST-Unbabel 2021 Submission for the Quality Estimation Shared Task

This paper describes Charles University sub-mission for Terminology translation shared task at WMT21. The objective of this task is to design a system which translates certain terms based on a provided terminology database, while preserving high overall translation quality. We competed in English-French language pair. Our approach is based on providing the desired translations alongside the input sentence and training the model to use these provided terms. We lemmatize the terms both during the training and inference, to allow the model to learn how to produce correct surface forms of the words, when they differ from the forms provided in the terminology database.

CUNI systems for WMT21: Multilingual Low-Resource Translation for Indo-European Languages Shared Task

This work introduces a simple regressive ensemble for evaluating machine translation quality based on a set of novel and established metrics. 
We evaluate the ensemble using a correlation to expert-based MQM scores of the WMT 2021 Metrics workshop.
In both monolingual and zero-shot cross-lingual settings, we show a significant performance improvement over single metrics.
In the cross-lingual settings, we also demonstrate that an ensemble approach is well-applicable to unseen languages.
Furthermore, we identify a strong reference-free baseline that consistently outperforms the commonly-used BLEU and METEOR measures and significantly improves our ensemble's performance.

Regressive Ensemble for Machine Translation Quality Evaluation

This paper presents the University of Edinburgh's constrained submissions of English-German and English-Hausa systems to the WMT 2021 shared task on news translation. We build En-De systems in three stages: corpus filtering, back-translation, and fine-tuning. For En-Ha we use an iterative back-translation approach on top of pre-trained En-De models and investigate vocabulary embedding mapping.

The {U}niversity of {E}dinburgh’s {E}nglish-{G}erman and {E}nglish-{H}ausa Submissions to the {WMT}21 News Translation Task

This paper provides an overview of NVIDIA NeMo's neural machine translation systems for the constrained data track of the WMT21 News and Biomedical Shared Translation Tasks. Our news task submissions for English-German (En-De) and English-Russian (En-Ru) are built on top of a baseline transformer-based sequence-to-sequence model \cite{vaswani2017}. Specifically, we use a combination of 1) checkpoint averaging 2) model scaling 3) data augmentation with backtranslation and knowledge distillation from right-to-left factorized models 4) finetuning on test sets from previous years 5) model ensembling 6) shallow fusion decoding with transformer language models and 7) noisy channel re-ranking. Additionally, our biomedical task submission for English $\leftrightarrow$ Russian uses a biomedically biased vocabulary and is trained from scratch on news task data, medically relevant text curated from the news task dataset, and biomedical data provided by the shared task. Our news system achieves a sacreBLEU score of 39.5 on the WMT'20 En-De test set outperforming the best submission from last year's task of 38.8. Our biomedical task Ru-En and En-Ru systems reach BLEU scores of 43.8 and 40.3 respectively on the WMT'20 Biomedical Task Test set, outperforming the previous year's best submissions.

NVIDIA NeMo's Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21

This paper describes the Air Force Research Laboratory (AFRL) machine translation sys- tems and the improvements that were developed during the WMT21 evaluation campaign. This year, we explore various methods of adapting our baseline models from WMT20 and again measure improvements in performance on the Russian–English language pair.

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