United Arab Emirates

Adversarial attack of structured prediction models faces various challenges such as the difficulty of perturbing discrete words, the sentence quality issue, and the sensitivity of outputs to small perturbations.  In this work, we introduce SHARP, a new attack method that formulates the black-box adversarial attack as a search-based optimization problem with a specially designed objective function considering sentence fluency, meaning preservation and attacking effectiveness. Additionally, three different searching strategies are analyzed and compared, \ie, Beam Search, Metropolis-Hastings Sampling, and Hybrid Search. We demonstrate the effectiveness of our attacking strategies on two challenging structured prediction tasks: part-of-speech (POS) tagging and dependency parsing. Through automatic and human evaluations, we show that our method performs a more potent attack compared with pioneer arts. Moreover, the generated adversarial examples can be used to successfully boost the robustness and performance of the victim model via adversarial training.

EMNLP 2022

Probing Script Knowledge from Pre-Trained Models

script knowledge pretrained models temporal knowledge

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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. 
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* 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.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
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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 Inria ALMAnaCH team submission to the WMT 2022 general translation shared task. Participating in the language directions \{cs,ru,uk\}→en and cs↔uk, we experiment with the use of a dedicated Latin-script transcription convention aimed at representing all Slavic languages involved in a way that maximises character- and word-level correspondences between them as well as with the English language. Our hypothesis was that bringing the source and target language closer could have a positive impact on machine translation results. We provide multiple comparisons, including bilingual and multilingual baselines, with and without transcription. Initial results indicate that the transcription strategy was not successful, resulting in lower results than baselines. We nevertheless submitted our multilingual, transcribed models as our primary systems, and in this paper provide some indications as to why we got these negative results.

Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation?

In this paper, we describe our NAIST-NICT-TIT submission to the WMT22 general machine translation task. We participated in this task for the English ↔ Japanese language pair.
Our system is characterized as an ensemble of Transformer big models, k-nearest-neighbor machine translation (kNN-MT) (Khandelwal et al., 2021), and reranking.

In our translation system, we construct the datastore for kNN-MT from back-translated monolingual data and integrate kNN-MT into the ensemble model. We designed a reranking system to select a translation from the n-best translation candidates generated by the translation system. We also use a context-aware model to improve the document-level consistency of the translation.

NAIST-NICT-TIT WMT22 General MT Task Submission

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.

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

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.

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Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation?

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SHARP: Search-Based Adversarial Attack for Structured Prediction

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PRESENTATIONS

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