United Arab Emirates

Automatic Speech Recognition (ASR) systems are taking over in different industries starting from producing video subtitles to interactive digital assistants. ASR output can be used in automatic indexing, categorizing, searching along with normal human readability. Raw transcripts from ASR systems are difficult to interpret since it usually produces text without punctuation and case information (all lower, all upper, camel case etc.), thus limiting the performance of downstream NLP tasks. We proposed an approach to restore the punctuation and case for both English and Hinglish (i.e Hindi vocabulary in Latin script) languages. We have performed a classification task using encoder-based transformers which is a mini BERT consisting of 4 encoder layers for punctuation and case restoration instead of the traditional Seq2Seq model considering the latency constraint in real world use cases. It consists of  a total number of 15 distinct classes for the model which includes 5 punctuations i.e Period(.), Comma(,), Single Quote(‘), Double Quote(&quot;) &amp; Question Mark(?) with different combinations of casing. The model is benchmarked on an internal dataset which was based on user conversation with the voice assistant and it achieves a F1(macro) score of 91.52% on the test set.

EMNLP 2022

Punctuation and case restoration in code mixed Indian languages

capitalization

punctuation

hinglish

indic nlp

seq2seq

text classification

bert

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

IJCNLP-AACL 2025

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

Automatic Speech Recognition (ASR) systems are taking over in different industries starting from producing video subtitles to interactive digital assistants. ASR output can be used in automatic indexing, categorizing, searching along with normal human readability. Raw transcripts from ASR systems are difficult to interpret since it usually produces text without punctuation and case information (all lower, all upper, camel case etc.), thus limiting the performance of downstream NLP tasks. We proposed an approach to restore the punctuation and case for both English and Hinglish (i.e Hindi vocabulary in Latin script) languages. We have performed a classification task using encoder-based transformers which is a mini BERT consisting of 4 encoder layers for punctuation and case restoration instead of the traditional Seq2Seq model considering the latency constraint in real world use cases. It consists of  a total number of 15 distinct classes for the model which includes 5 punctuations i.e Period(.), Comma(,), Single Quote(‘), Double Quote(") & Question Mark(?) with different combinations of casing. The model is benchmarked on an internal dataset which was based on user conversation with the voice assistant and it achieves a F1(macro) score of 91.52% on the test set.

workshop paper

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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.

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.

Probing Script Knowledge from Pre-Trained Models

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.

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