United Arab Emirates

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&#39;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.

EMNLP 2022

eTranslation&#39;s Submissions to the WMT22 General Machine Translation Task

fine tuning

multilingual model

model ensemble

back-translation

data filtering

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

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

workshop paper

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022!  I believe this conference has been complicated beyond any precedent.  Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm.  We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community.  Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well:  we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

<br>*Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA*<br>
***EMNLP 2022 General Chair***
<br><br><br>
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Register here

Welcome!
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 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.

HW-TSC's Submissions to the WMT 2022 General Machine Translation Shared Task

We participated in the WMT General MT task and focus on four high resource language pairs: English to Chinese, Chinese to English, English to Japanese and Japanese to English). The submitted systems (LanguageX) focus on data cleaning, data selection, data mixing and TM-augmented NMT. Rules and multilingual language model are used for data filtering and data selection. In the automatic evaluation, our best submitted English to Chinese system achieved 54.3 BLEU score and 63.8 COMET score, which is the highest among all the submissions.

No Domain Left behind

This paper summarizes the results of our test suite evaluation with a main focus on morphology for the language pairs English to/from German. We look at the translation of morphologically complex words (DE–EN), and evaluate
whether English noun phrases are translated as compounds vs. phrases into German. Furthermore, we investigate the preservation of morphological features (gender in EN–DE pronoun translation and number in morpho-syntactically
complex structures for DE–EN). Our results indicate that systems are able to interpret linguistic structures to obtain relevant information, but also that translation becomes more challenging with increasing complexity, as seen, for example, when translating words with negation or non-concatenative properties, and for the more
complex cases of the pronoun translation task.

Test Suite Evaluation: Morphological Challenges and Pronoun Translation

Automatic translations with critical errors may lead to misinterpretations and pose several risks for the user. As such, it is important that Machine Translation (MT) Evaluation systems are robust to these errors in order to increase the reliability and safety of Machine Translation systems. Here we introduce SMAUG a novel Sentence-level Multilingual AUGmentation approach for generating translations with critical errors and apply this approach to create a test set to evaluate the robustness of MT metrics to these errors. We show that current State-of-the-Art metrics are improving their capability to distinguish translations with and without critical errors and to penalize the first accordingly. We also show that metrics tend to struggle with errors related to named entities and numbers and that there is a high variance in the robustness of current methods to translations with critical errors.

Robust MT Evaluation with Sentence-level Multilingual Augmentation

As machine translation (MT) metrics improve their correlation with human judgement every year, it is crucial to understand the limitations of these metrics at the segment level. Specifically, it is important to investigate metric behaviour when facing accuracy errors in MT because these can have dangerous consequences in certain contexts (e.g., legal, medical). We curate ACES, a translation accuracy challenge set, consisting of 68 phenomena ranging from simple perturbations at the word/character level to more complex errors based on discourse and real-world knowledge. We use ACES to evaluate a wide range of MT metrics including the submissions to the WMT 2022 metrics shared task and perform several analyses leading to general recommendations for metric developers. We recommend: a) combining metrics with different strengths, b) developing metrics that give more weight to the source and less to surface-level overlap with the reference and c) explicitly modelling additional language-specific information beyond what is available via multilingual embeddings.

ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics

Contextual word embeddings extracted from pre-trained models have become the basis for many downstream NLP tasks, including machine translation automatic evaluations. Metrics that leverage embeddings claim better capture of synonyms and changes in word orders, and thus better correlation with human ratings than surface-form matching metrics (e.g. BLEU). However, few studies have been done to examine robustness of these metrics. This report uses a challenge set to uncover the brittleness of reference-based and reference-free metrics. Our challenge set1 aims at examining metrics' capability to correlate synonyms in different areas and to discern catastrophic errors at both word- and sentence-levels. The results show that although embedding-based metrics perform relatively well on discerning sentence-level negation/affirmation errors, their performances on relating synonyms are poor. In addition, we find that some metrics are susceptible to text styles so their generalizability compromised.

Exploring Robustness of Machine Translation Metrics: A Study of Twenty-Two Automatic Metrics in the WMT22 Metric Task

We develop two new metrics that build on top of the COMET architecture. 
The main contribution is collecting a ten-times larger corpus of human judgements than COMET and investigating how to filter out problematic human judgements. We propose filtering human judgements where human reference is statistically worse than machine translation. Furthermore, we average scores of all equal segments evaluated multiple times.
The results comparing automatic metrics on source-based DA and MQM-style human judgement show state-of-the-art performance on a system-level pair-wise system ranking.
We release both of our metrics for public use.

MS-COMET: More and Better Human Judgements Improve Metric Performance

In this paper, we describe our submission to the WMT22 metrics shared task. Our metric focuses on computing contextual and syntactic equivalences along with lexical, morphological, and semantic similarity. The intent is to capture the fluency and context of the MT outputs along with their adequacy. Fluency is captured using syntactic similarity and context is captured using sentence similarity leveraging sentence embeddings. The final sentence translation score is the weighted combination of three similarity scores: a) Syntactic Similarity b) Lexical, Morphological and Semantic Similarity, and c) Contextual Similarity. This paper outlines two improved versions of MEE i.e., MEE2 and MEE4. Additionally, we report our experiments on language pairs of en-de, en-ru and zh-en from WMT17-19 testset and further depict the correlation with human assessments.

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The NiuTrans Machine Translation Systems for WMT22

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