United Arab Emirates

This paper describes Huawei Artificial Intelligence Application Research Center&#39;s neural machine translation system (&quot;BabelTar&quot;). Our submission to the WMT22 biomedical translation shared task covers language directions between English and the other seven languages (French, German, Italian, Spanish, Portuguese, Russian, and Chinese). During the past four years, our participation in this domain-specific track has witnessed a paradigm shift of methodology from a purely data-driven focus to embracing diversified techniques, including pre-trained multilingual NMT models, homograph disambiguation, ensemble learning, and preprocessing methods. We illustrate practical insights and measured performance improvements relating to how we further improve our domain-specific NMT system.

EMNLP 2022

Huawei BabelTar NMT at WMT22 Biomedical Translation Task: How We Further Improve Domain-specific NMT

domain adaption; terminology; homograph disambiguation

This paper describes Huawei Artificial Intelligence Application Research Center's neural machine translation system ("BabelTar"). Our submission to the WMT22 biomedical translation shared task covers language directions between English and the other seven languages (French, German, Italian, Spanish, Portuguese, Russian, and Chinese). During the past four years, our participation in this domain-specific track has witnessed a paradigm shift of methodology from a purely data-driven focus to embracing diversified techniques, including pre-trained multilingual NMT models, homograph disambiguation, ensemble learning, and preprocessing methods. We illustrate practical insights and measured performance improvements relating to how we further improve our domain-specific NMT system.

workshop paper

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This paper describes the translation systems trained by Huawei translation services center (HW-TSC) for the WMT22 biomedical translation task in five language pairs: English↔German (en↔de), English↔French (en↔fr), English↔Chinese (en↔zh), English↔Russian (en↔ru) and Spanish→English (es→en). Our primary systems are built on deep Transformer with a large filter size. We also utilize R-Drop, data diversification, forward translation, back translation, data selection, finetuning and ensemble to improve the system performance. According to the official evaluation results in OCELoT or CodaLab, our unconstrained systems in en→de, de→en, en→fr, fr→en, en→zh and es→en (clinical terminology sub-track) get the highest BLEU scores among all submissions for the WMT22 biomedical translation task.

HW-TSC Translation Systems for the WMT22 Biomedical Translation Task

We present the joint contribution of IST and Unbabel to the WMT 2022 Chat Translation Shared Task. We participated in all six language directions (English ↔ German, English ↔ French, English ↔ Brazilian Portuguese). Due to the lack of domain-specific data, we use mBART50, a large pretrained language model trained on millions of sentence-pairs, as our base model. We fine-tune it using a two step fine-tuning process. In the first step, we fine-tune the model on publicly available data. In the second step, we use the validation set. After having a domain specific model, we explore the use of kNN-MT as a way of incorporating domain-specific data at decoding time.

Unbabel-IST at the WMT Chat Translation Shared Task

Multilingual chatbots are the need of the hour for modern business. There is increasing demand for such systems all over the world. A multilingual chatbot can help to connect distant parts of the world together, without sharing a common language. We participated in WMT22 Chat Translation Shared Task. In this paper, we report descriptions of methodologies used for participation. We submit outputs from multi-encoder based transformer model, where one encoder is for context and another for source utterance. We consider one previous utterance as context. We obtain COMET scores of 0.768 and 0.907 on English-to-German and German-to-English directions, respectively. We submitted outputs without using context at all, which generated worse results in English-to-German direction. While for German-to-English, the model achieved a lower COMET score but slightly higher chrF and BLEU scores. Further, to understand the effectiveness of the context encoder, we submitted a run after removing the context encoder during testing and we obtain similar results.

Investigating Effectiveness of Multi-Encoder for Conversational Neural Machine Translation

This paper introduces the joint submission of the Beijing Jiaotong University and WeChat AI to the WMT'22 chat translation task for English\textless{}-\textgreater{}German. Based on the Transformer, we apply several effective variants. In our experiments, we apply the pre-training-then-fine-tuning paradigm. In the first pre-training stage, we employ data filtering and synthetic data generation (i.e., back-translation, forward-translation, and knowledge distillation). In the second fine-tuning stage, we investigate speaker-aware in-domain data generation, speaker adaptation, prompt-based context modeling, target denoising fine-tuning, and boosted self-COMET-based model ensemble. Our systems achieve 81.0 and 94.6 COMET scores on English-\textgreater{}German and German-\textgreater{}English, respectively. The COMET scores of English-\textgreater{}German and German-\textgreater{}English are the highest among all submissions.

BJTU-WeChat's Systems for the WMT22 Chat Translation Task

This paper describes the submissions of Huawei Translation Services Center (HW-TSC) to  WMT22 chat translation shared task on English\textless{}-\textgreater{}Germany (en-de) bidirection with results of zore-shot and few-shot tracks. We use the deep transformer architecture with a lager parameter size. Our submissions to the WMT21 News Translation task are used as the baselines. We adopt strategies such as back translation, forward translation, domain transfer, data selection, and noisy forward translation in task, and achieve competitive results on the development set. We also test the effectiveness of document translation on chat tasks. Due to the lack of chat data, the results on the development set show that it is not as effective as sentence-level translation models.

HW-TSC Translation Systems for the WMT22 Chat Translation Task

This paper describes Microsoft's submission to the first shared task on sign language translation at WMT 2022, a public competition tackling sign language to spoken language translation for Swiss German sign language. The task is very challenging due to data scarcity and an unprecedented vocabulary size of more than 20k words on the target side. Moreover, the data is taken from real broadcast news, includes native signing and covers scenarios of long videos. Motivated by recent advances in action recognition, we incorporate full body information by extracting features from a pre-trained I3D model and applying a standard transformer network. The accuracy of the system is further improved by applying careful data cleaning on the target text. We obtain BLEU scores of 0.6 and 0.78 on the test and dev set respectively, which is the best score among the participants of the shared task. Also in the human evaluation the submission reaches the first place. The BLEU score is further improved to 1.08 on the dev set by applying features extracted from a lip reading model.

Clean Text and Full-Body Transformer: Microsoft's Submission to the WMT22 Shared Task on Sign Language Translation

This paper describes the DFKI-MLT submission to the WMT-SLT 2022 sign language translation (SLT) task from Swiss German Sign Language (video) into German (text). State-of-the-art techniques for SLT use a generic seq2seq architecture with customized input embeddings. Instead of word embeddings as used in textual machine translation, SLT systems use features extracted from video frames. Standard approaches often do not benefit from temporal features. In our participation, we present a system that learns spatio-temporal feature representations and translation in a single model, resulting in a real end-to-end architecture expected to better generalize to new data sets. Our best system achieved \$5\textbackslash{}pm1\$ BLEU points on the development set, but the performance on the test dropped to \$0.11\textbackslash{}pm0.06\$ BLEU points.

Spatio-temporal Sign Language Representation and Translation

We describe TTIC's model submission to WMT-SLT 2022 task on sign language translation (Swiss-German Sign Language (DSGS) -\textgreater{} German). Our model consists of an I3D backbone for image encoding and a Transformerbased encoder-decoder model for sequence modeling. The I3D is pre-trained with isolated sign recognition using the WLASL dataset. The model is based on RGB images alone and does not rely on the pre-extracted human pose. We explore a few different strategies for model training in this paper. Our system achieves 0.3 BLEU score and 0.195 Chrf score on the official test set.

TTIC's WMT-SLT 22 Sign Language Translation System

This paper describes the system developed at the Universitat Politècnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction. 
We use a Transformer model implemented with the Fairseq modeling toolkit. We have experimented with the vocabulary size, data augmentation techniques and pretraining the model with the PHOENIX-14T dataset. 
Our system obtains 0.50 BLEU score for the test set, improving the organizers' baseline by 0.38 BLEU. We remark the poor results for both the baseline and our system, and thus, the unreliability of our findings.

Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22

We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work describes our approach, which is based on filtering the given noisy data using a sentence-pair classifier that was built by fine-tuning a pre-trained language model. To train the classifier, we obtain positive samples (i.e. high-quality parallel sentences) from a gold-standard curated dataset and extract negative samples (i.e. low-quality parallel sentences) from automatically aligned parallel data by choosing sentences with low alignment scores. Our final machine translation model was then trained on filtered data, instead of the entire noisy dataset. We empirically validate our approach by evaluating on two common datasets and show that data filtering generally improves overall translation quality, in some cases even significantly.

Huawei BabelTar NMT at WMT22 Biomedical Translation Task: How We Further Improve Domain-specific NMT

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Huawei BabelTar NMT at WMT22 Biomedical Translation Task: How We Further Improve Domain-specific NMT

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Next from EMNLP 2022

HW-TSC Translation Systems for the WMT22 Biomedical Translation Task

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PRESENTATIONS

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COMPANY

RESOURCES

Downloads