United Arab Emirates

This paper describes Tencent AI Lab - Shanghai Jiao Tong University (TAL-SJTU) Low-Resource Translation systems for the WMT22 shared task. 
We participate in the general translation task on English-Livonian.
Our system is based on M2M100 with novel techniques that adapt it to the target language pair.
(1) Cross-model word embedding alignment: inspired by cross-lingual word embedding alignment, we successfully transfer a pre-trained word embedding to M2M100, enabling it to support Livonian.
(2) Gradual adaptation strategy: we exploit Estonian and Latvian as auxiliary languages for many-to-many translation training and then adapt to English-Livonian.
(3) Data augmentation: to enlarge the parallel data for English-Livonian, we construct pseudo-parallel data with Estonian and Latvian as pivot languages.
(4) Fine-tuning: to make the most of all available data, we fine-tune the model with the validation set and online back-translation, further boosting the performance.
In model evaluation: (1) We find that previous work underestimated the translation performance of Livonian due to inconsistent Unicode normalization, which may cause a discrepancy of up to 14.9 BLEU score.
(2) In addition to the standard validation set, we also employ round-trip BLEU to evaluate the models, which we find more appropriate for this task. 
Finally, our unconstrained system achieves BLEU scores of 17.0 and 30.4 for English to/from Livonian.

EMNLP 2022

Tencent AI Lab - Shanghai Jiao Tong University Low-Resource Translation System for the WMT22 Translation Task

low resource

transfer learning

machine translation

# Welcome everyone to ACL 2022!

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ACL 2022

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

This paper describes Lan-Bridge Translation systems for the WMT 2022 General Translation shared task. We participate in 18 language directions: English to and from Czech, German, Ukrainian, Japanese, Russian, Chinese, English to Croatian, French to German, Yakut to and from Russian and Ukrainian to and from Czech.To develop systems covering all these direc\_x0002\_tions, we mainly focus on multilingual mod\_x0002\_els. In general, we apply data corpus filtering, scaling model size, sparse expert model (in par\_x0002\_ticular, Transformer with adapters), large scale backtranslation and language model reranking techniques. Our system ranks first in 6 directions based on automatic evaluation.

Lan-Bridge MT's Participation in the WMT 2022 General Translation Shared Task

Manifold's English-Chinese System at WMT22 is an ensemble of 4 models trained by different configurations with scheduled sampling-based fine-tuning. The four configurations are DeepBig (XenC), DeepLarger (XenC), DeepBig-TalkingHeads (XenC) and DeepBig (LaBSE). Concretely, DeepBig extends Transformer-Big to 24 encoder layers. DeepLarger has 20 encoder layers and its feed-forward network (FFN) dimension is 8192. TalkingHeads applies the talking-heads trick. For XenC configs, we selected monolingual and parallel data that is similar to the past newstest datasets using XenC, and for LaBSE, we cleaned the officially provided parallel data using LaBSE pretrained model. According to the officially released autonomic metrics leaderboard, our final constrained system ranked 1st among all others when evaluated by bleu-all, chrf-all and COMET-B, 2nd by COMET-A.

Manifold's English-Chinese System at WMT22 General MT Task

We here describe our neural machine translation system for general machine translation shared task in WMT 2022. Our systems are based on the Transformer (Vaswani et al., 2017) with base settings. We explore the high-efficiency model training strategies, aimed to train a model with high-accuracy by using small model and a reasonable amount of data. We performed fine-tuning and ensembling with N-best ranking in English to/from Japanese directions. We found that fine-tuning by filtered JParaCrawl data set leads to better translations for both of direction in English to/from Japanese models. In English to Japanese direction model, ensembling and N-best ranking of 10 different checkpoints improved translations. By comparing with other online translation service, we found that our model achieved a great translation quality.

KYB General Machine Translation Systems for WMT22

Customer feedback can be an important signal for improving commercial machine translation systems. One solution for fixing specific translation errors is to remove the related erroneous training instances followed by re-training of the machine translation system, which we refer to as instance-specific data filtering. Influence functions (IF) have been shown to be effective in finding such relevant training examples for classification tasks such as image classification, toxic speech detection and entailment task. Given a probing instance, IF find influential training examples by measuring the similarity of the probing instance with a set of training examples in gradient space. In this work, we examine the use of influence functions for Neural Machine Translation (NMT). We propose two effective extensions to a state of the art influence function and demonstrate on the sub-problem of copied training examples that IF can be applied more generally than hand-crafted regular expressions.

Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation

This paper describes AISP-SJTU's participation in WMT 2022 shared general MT task. In this shared task, we participated in four translation directions: English-\textgreater{}Chinese, Chinese-\textgreater{}English, English-\textgreater{}Japanese and Japanese-\textgreater{}English. Our systems are based on the Transformer architecture with several novel and effective variants, including network depth and internal structure. In our experiments, we employ data filtering, large-scale back-translation, knowledge distillation, forward-translation, iterative in-domain knowledge finetune and model ensemble. The constrained systems achieve 48.8, 29.7, 39.3 and 22.0 case-sensitive BLEU scores on EN-\textgreater{}ZH, ZH-\textgreater{}EN, EN-\textgreater{}JA and JA-\textgreater{}EN, respectively.

The AISP-SJTU Translation System for WMT 2022

This submission to the WMT22: General MT Task, consists of translations produced from a series of NMT models of the following two language pairs: german-to-english and german-to-french.  All the models are trained using only the parallel training data specified by WMT22, and no monolingual training data was used. The models follow the transformer architecture employing 8 attention heads and 6 layers in both the encoder and decoder. It is also worth mentioning that, in order to limit the computational resources that we would use during the training process, we decided to train the majority of models by limiting the training to 21 epochs. Moreover, the translations submitted at WMT22 have been produced using the test data released by the WMT22.
The aim of our experiments has been to evaluate methods for cleaning-up a parallel corpus to determine if this will lead to a translation model producing more accurate translations. For each language pair, the base NMT models has been trained from raw parallel training corpora, while the additional NMT models have been trained with corpora subjected to a special cleaning process with the following tools: Bifixer and Bicleaner. It should be mentioned that the Bicleaner repository doesn't provide pre-trained classifiers for the above language pairs, consequently we trained probabilistic dictionaries in order to produce new models. The fundamental differences between these NMT models produced are mainly related to the quality and the quantity of the training data, while there are very few differences in the training parameters. 
To complete this work, we used the following three software packages: (i) MARIAN NMT (Version: v1.11.5), which was used for the training of the neural machine translation models and (ii) Bifixer and (iii) Bicleaner, which were used in order to correct and clean the parallel training data. Concerning the Bifixer and Bicleaner tools, we followed all the steps as described meticulously in the following article: "Ramírez-Sánchez, G., Zaragoza-Bernabeu, J., Bañón, M., \& Rojas, S.O. (2020). Bifixer and Bicleaner: two open-source tools to clean your parallel data. EAMT. '' and also in the official github pages: https://github.com/bitextor/bifixer, https://github.com/bitextor/bicleaner.

Evaluating Corpus Cleanup Methods in the WMT'22 News Translation Task

The paper describes the NMT models for French-German, English-Ukranian and English-Russian, submitted by the
  eTranslation team to the WMT22 general machine translation shared
  task.  In the WMT news task last year, multilingual systems with
  deep and complex architectures utilizing immense amount of data and
  resources were dominant. This year with the task extended to cover
  less domain specific text we expected even more dominance of such
  systems. In the hope to produce competitive (constrained) systems
  despite our limited resources, this time we selected only medium
  resource language pairs, which are serviced in the European
  Commission's eTranslation system. We took the approach of exploring
  less resource intensive strategies focusing on data selection and
  filtering to improve the performance of baseline systems. With our
  submitted systems our approach scored competitively according to the
  automatic rankings, except for the the English--Russian model where
  our submission was only a baseline reference model developed as a
  by-product of the multilingual setup we built focusing primarily on
  the English-Ukranian language pair.

eTranslation's Submissions to the WMT22 General Machine Translation Task

This paper describes the NiuTrans neural machine translation systems of the WMT22 General MT constrained task. We participate in four directions, including Chinese→English, English→Croatian, and Livonian↔English. Our models are based on several advanced Transformer variants, e.g., Transformer-ODE, Universal Multiscale Transformer (UMST). The main workflow consists of data filtering, large-scale data augmentation (i.e., iterative back-translation, iterative knowledge distillation), and specific-domain fine-tuning. Moreover, we try several multi-domain methods, such as a multi-domain model structure and a multi-domain data clustering method, to rise to this year's newly proposed multi-domain test set challenge. For low-resource scenarios, we build a multi-language translation model to enhance the performance, and try to use the pre-trained language model (mBERT) to initialize the translation model.

The NiuTrans Machine Translation Systems for WMT22

This paper describes DUTNLP Lab's submission to the WMT22 General MT Task on four translation directions: English to/from Chinese and English to/from Japanese under the constrained condition.
Our primary system are built on several Transformer variants which employ wider FFN layer or deeper encoder layer. 
The bilingual data are filtered by detailed data pre-processing strategies and four data augmentation methods are combined to enlarge the training data with the provided monolingual data.
Several common methods are also employed to further improve the model performance, such as fine-tuning, model ensemble and post-editing.
As a result, our constrained systems achieve 29.01, 63.87, 41.84, and 24.82 BLEU scores on Chinese-to-English, English-to-Chinese, English-to-Japanese, and Japanese-to-English, respectively.

DUTNLP Machine Translation System for WMT22 General MT Task

This paper presents the submissions of Huawei Translate Services Center (HW-TSC) to the WMT 2022 General Machine Translation Shared Task. We participate in 6 language pairs, including Zh↔En, Ru↔En, Uk↔En, Hr↔En, Uk↔Cs and Liv↔En. We use Transformer architecture and obtain the best performance via multiple variants with larger parameter sizes. We perform fine-grained pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. For medium and highresource languages, we mainly use data augmentation strategies, including Back Translation, Self Training, Ensemble Knowledge Distillation, Multilingual, etc. For low-resource languages such as Liv, we use pre-trained machine translation models, and then continue training with Regularization Dropout (R-Drop). The previous mentioned data augmentation methods are also used. Our submissions obtain competitive results in the final evaluation.

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