GenAI4ELab/papercli-papers huggingface dataset:
The SETU-ADAPT Submissions to the WMT24 Low-Resource Indic Language Translation Task
Authors: Neha Gajakos; Prashanth Nayak; Rejwanul Haque; Andy Way
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper presents the SETU-ADAPT’s submissions to the WMT 2024 Low-Resource Indic Language Translation task. We participated in the unconstrained segment of the task, focusing on the Assamese-to-English and English-to-Assamese language pairs. Our approach involves leveraging Large Language Models (LLMs) as the baseline systems for all our MT tasks. Furthermore, we applied various strategies to improve the baseline systems. In our first approach, we fine-tuned LLMs using all the data provided by the task organisers. Our second approach explores in-context learning by focusing on few-shot prompting. In our final approach we explore an efficient data extraction technique based on a fuzzy match-based similarity measure for fine-tuning. We evaluated our systems using BLEU, chrF, WER, and COMET. The experimental results showed that our strategies can effectively improve the quality of translations in low-resource scenarios.
SPRING Lab IITM’s Submission to Low Resource Indic Language Translation Shared Task
Authors: Hamees Sayed; Advait Joglekar; Srinivasan Umesh
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: We develop a robust translation model for four low-resource Indic languages: Khasi, Mizo, Manipuri, and Assamese. Our approach includes a comprehensive pipeline from data collection and preprocessing to training and evaluation, leveraging data from WMT task datasets, BPCC, PMIndia, and OpenLanguageData. To address the scarcity of bilingual data, we use back-translation techniques on monolingual datasets for Mizo and Khasi, significantly expanding our training corpus. We fine-tune the pre-trained NLLB 3.3B model for Assamese, Mizo, and Manipuri, achieving improved performance over the baseline. For Khasi, which is not supported by the NLLB model, we introduce special tokens and train the model on our Khasi corpus. Our training involves masked language modelling, followed by fine-tuning for English-to-Indic and Indic-to-English translations.
Machine Translation Advancements of Low-Resource Indian Languages by Transfer Learning
Authors: Bin Wei; Zheng Jiawei; Zongyao Li; Zhanglin Wu; Jiaxin Guo; Daimeng Wei; Zhiqiang Rao; Shaojun Li; Yuanchang Luo; Hengchao Shang; Jinlong Yang; Yuhao Xie; Hao Yang
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper introduces the submission by Huawei Translation Center (HW-TSC) to the WMT24 Indian Languages Machine Translation (MT) Shared Task. To develop a reliable machine translation system for low-resource Indian languages, we employed two distinct knowledge transfer strategies, taking into account the characteristics of the language scripts and the support available from existing open-source models for Indian languages. For Assamese(as) and Manipuri(mn), we fine-tuned the existing IndicTrans2 open-source model to enable bidirectional translation between English and these languages. For Khasi(kh) and Mizo(mz), we trained a multilingual model as the baseline using bilingual data from this four language pairs as well as additional Bengali data, which share the same language family. This was followed by fine-tuning to achieve bidirectional translation between English and Khasi, as well as English and Mizo. Our transfer learning experiments produced significant results: 23.5 BLEU for en→as, 31.8 BLEU for en→mn, 36.2 BLEU for as→en, and 47.9 BLEU for mn→en on their respective test sets. Similarly, the multilingual model transfer learning experiments yielded impressive outcomes, achieving 19.7 BLEU for en→kh, 32.8 BLEU for en→mz, 16.1 BLEU for kh→en, and 33.9 BLEU for mz→en on their respective test sets. These results not only highlight the effectiveness of transfer learning techniques for low-resource languages but also contribute to advancing machine translation capabilities for low-resource Indian languages.
NLIP_Lab-IITH Low-Resource MT System for WMT24 Indic MT Shared Task
Authors: Pramit Sahoo; Maharaj Brahma; Maunendra Sankar Desarkar
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: In this paper, we describe our system for the WMT 24 shared task of Low-Resource Indic Language Translation. We consider eng↔{as, kha, lus, mni} as participating language pairs. In this shared task, we explore the fine-tuning of a pre-trained model motivated by the pre-trained objective of aligning embeddings closer by alignment augmentation (Lin et al.,2020) for 22 scheduled Indian languages. Our primary system is based on language-specific finetuning on a pre-trained model. We achieve chrF2 scores of 50.6, 42.3, 54.9, and 66.3 on the official public test set for eng→as, eng→kha, eng→lus, eng→mni respectively. We also explore multilingual training with/without language grouping and layer-freezing.
Yes-MT’s Submission to the Low-Resource Indic Language Translation Shared Task in WMT 2024
Authors: Yash Bhaskar; Parameswari Krishnamurthy
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper presents the systems submitted by the Yes-MT team for the Low-Resource Indic Language Translation Shared Task at WMT 2024, focusing on translating between English and the Assamese, Mizo, Khasi, and Manipuri languages. The experiments explored various approaches, including fine-tuning pre-trained models like mT5 and IndicBart in both Multilingual and Monolingual settings, LoRA finetune IndicTrans2, zero-shot and few-shot prompting with large language models (LLMs) like Llama 3 and Mixtral 8x7b, LoRA Supervised Fine Tuning Llama 3, and training Transformers from scratch. The results were evaluated on the WMT23 Low-Resource Indic Language Translation Shared Task’s test data using SacreBLEU and CHRF highlighting the challenges of low-resource translation and show the potential of LLMs for these tasks, particularly with fine-tuning.
System Description of BV-SLP for Sindhi-English Machine Translation in MultiIndic22MT 2024 Shared Task
Authors: Nisheeth Joshi; Pragya Katyayan; Palak Arora; Bharti Nathani
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper presents our machine translation system that was developed for the WAT2024 MultiInidc MT shared task. We built our system for the Sindhi-English language pair. We developed two MT systems. The first system was our baseline system where Sindhi was translated into English. In the second system we used Hindi as a pivot for the translation of text. In both the cases we had identified the name entities and translated them into English as a preprocessing step. Once this was done, the standard NMT process was followed to train and generate MT outputs for the task. The systems were tested on the hidden dataset of the shared task
WMT24 System Description for the MultiIndic22MT Shared Task on Manipuri Language
Authors: Ningthoujam Justwant Singh; Kshetrimayum Boynao Singh; Ningthoujam Avichandra Singh; Sanjita Phijam; Thoudam Doren Singh
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper presents a Transformer-based Neural Machine Translation (NMT) system developed by the Centre for Natural Language Processing and the Department of Computer Science and Engineering at the National Institute of Technology Silchar, India (NITS-CNLP) for the MultiIndic22MT 2024 Shared Task. The system focused on the English-Manipuri language pair for the WMT24 shared task. The proposed WMT system shows a BLEU score of 6.4, a chrF score of 28.6, and a chrF++ score of 26.6 on the public test set Indic-Conv dataset. Further, in the public test set Indic-Gen dataset, it achieved a BLEU score of 8.1, a chrF score of 32.1, and a chrF++ score of 29.4 on the English-to-Manipuri translation.
NLIP-Lab-IITH Multilingual MT System for WAT24 MT Shared Task
Authors: Maharaj Brahma; Pramit Sahoo; Maunendra Sankar Desarkar
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper describes NLIP Lab’s multilingual machine translation system for the WAT24 shared task on multilingual Indic MT task for 22 scheduled languages belonging to 4 language families. We explore pre-training for Indic languages using alignment agreement objectives. We utilize bi-lingual dictionaries to substitute words from source sentences. Furthermore, we fine-tuned language direction-specific multilingual translation models using small and high-quality seed data. Our primary submission is a 243M parameters multilingual translation model covering 22 Indic languages. In the IN22-Gen benchmark, we achieved an average chrF++ score of 46.80 and 18.19 BLEU score for the En-Indic direction. In the Indic-En direction, we achieved an average chrF++ score of 56.34 and 30.82 BLEU score. In the In22-Conv benchmark, we achieved an average chrF++ score of 43.43 and BLEU score of 16.58 in the En-Indic direction, and in the Indic-En direction, we achieved an average of 52.44 and 29.77 for chrF++ and BLEU respectively. Our model is competitive with IndicTransv1 (474M parameter model).
DCU ADAPT at WMT24: English to Low-resource Multi-Modal Translation Task
Authors: Sami Haq; Rudali Huidrom; Sheila Castilho
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper presents the system description of “DCU_NMT’s” submission to the WMT-WAT24 English-to-Low-Resource Multimodal Translation Task. We participated in the English-to-Hindi track, developing both text-only and multimodal neural machine translation (NMT) systems. The text-only systems were trained from scratch on constrained data and augmented with back-translated data. For the multimodal approach, we implemented a context-aware transformer model that integrates visual features as additional contextual information. Specifically, image descriptions generated by an image captioning model were encoded using BERT and concatenated with the textual input.The results indicate that our multimodal system, trained solely on limited data, showed improvements over the text-only baseline in both the challenge and evaluation sets, suggesting the potential benefits of incorporating visual information.
English-to-Low-Resource Translation: A Multimodal Approach for Hindi, Malayalam, Bengali, and Hausa
Authors: Ali Hatami; Shubhanker Banerjee; Mihael Arcan; Paul Buitelaar; John Philip McCrae
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: Multimodal machine translation leverages multiple data modalities to enhance translation quality, particularly for low-resourced languages. This paper uses a Multimodal model that integrates visual information with textual data to improve translation accuracy from English to Hindi, Malayalam, Bengali, and Hausa. This approach employs a gated fusion mechanism to effectively combine the outputs of textual and visual encoders, enabling more nuanced translations that consider both language and contextual visual cues. The performance of the multimodal model was evaluated against the text-only machine translation model based on BLEU, ChrF2 and TER. Experimental results demonstrate that the multimodal approach consistently outperforms the text-only baseline, highlighting the potential of integrating visual information in low-resourced language translation tasks.
OdiaGenAI’s Participation in WMT2024 English-to-Low Resource Multimodal Translation Task
Authors: Shantipriya Parida; Shashikanta Sahoo; Sambit Sekhar; Upendra Jena; Sushovan Jena; Kusum Lata
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper covers the system description of the team “ODIAGEN’s” submission to the WMT~2024 English-to-Low-Resource Multimodal Translation Task. We participated in the English-to-Low Resource Multimodal Translation Task, in two of the tasks, i.e. Text-only Translation and Multi-modal Translation. For Text-only Translation, we trained the Mistral-7B model for English to Multi-lingual (Hindi, Bengali, Malayalam, Hausa). For Multi-modal Translation (using both image and text), we trained the PaliGemma-3B model for English to Hindi translation.
Arewa NLP’s Participation at WMT24
Authors: Mahmoud Ahmad; Auwal Khalid; Lukman Aliyu; Babangida Sani; Mariya Abdullahi
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper presents the work of our team, “ArewaNLP,” for the WMT 2024 shared task. The paper describes the system submitted to the Ninth Conference on Machine Translation (WMT24). We participated in the English-Hausa text-only translation task. We fine-tuned the OPUS-MT-en-ha transformer model and our submission achieved competitive results in this task. We achieve a BLUE score of 27.76, 40.31 and 5.85 on the Development Test, Evaluation Test and Challenge Test respectively.
Multimodal Machine Translation for Low-Resource Indic Languages: A Chain-of-Thought Approach Using Large Language Models
Authors: Pawan Rajpoot; Nagaraj Bhat; Ashish Shrivastava
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper presents the approach and results of team v036 in the English-to-Low-Resource Multi-Modal Translation Task at the Ninth Conference on Machine Translation (WMT24). Our team tackled the challenge of translating English source text to low-resource Indic languages, specifically Hindi, Malayalam, and Bengali, while leveraging visual context provided alongside the text data. We used InternVL2 for extracting the image context along with Knowledge Distillation from bigger LLMs to train Small Language Model on the tranlsation task. During current shared task phase, we submitted best models (for this task), and overall we got rank 3 on Hindi, Bengali, and Malyalam datasets. We also open source our models on huggingface.
Chitranuvad: Adapting Multi-lingual LLMs for Multimodal Translation
Authors: Shaharukh Khan; Ayush Tarun; Ali Faraz; Palash Kamble; Vivek Dahiya; Praveen Pokala; Ashish Kulkarni; Chandra Khatri; Abhinav Ravi; Shubham Agarwal
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: In this work, we provide the system description of our submission as part of the English-to-Lowres Multimodal Translation Task at theWorkshop on Asian Translation (WAT2024). We introduce Chitranuvad, a multimodal model that effectively integrates Multilingual LLMand a vision module for Multimodal Translation. Our method uses a ViT image encoder to extract visual representations as visual tokenembeddings which are projected to the LLM space by an adapter layer and generates translation in an autoregressive fashion. We participated in all the three tracks (Image Captioning, Text-only and Multimodal translationtasks) for Indic languages (ie. English translation to Hindi, Bengali and Malyalam) and achieved SOTA results for Hindi in all of themon the Challenge set while remaining competitive for the other languages in the shared task.
Brotherhood at WMT 2024: Leveraging LLM-Generated Contextual Conversations for Cross-Lingual Image Captioning
Authors: Siddharth Betala; Ishan Chokshi
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: In this paper, we describe our system under the team name Brotherhood for the English-to-Lowres Multi-Modal Translation Task. We participate in the multi-modal translation tasks for English-Hindi, English-Hausa, English-Bengali, and English-Malayalam language pairs. We present a method leveraging multi-modal Large Language Models (LLMs), specifically GPT-4o and Claude 3.5 Sonnet, to enhance cross-lingual image captioning without traditional training or fine-tuning.Our approach utilizes instruction-tuned prompting to generate rich, contextual conversations about cropped images, using their English captions as additional context. These synthetic conversations are then translated into the target languages. Finally, we employ a weighted prompting strategy, balancing the original English caption with the translated conversation to generate captions in the target language.This method achieved competitive results, scoring 37.90 BLEU on the English-Hindi Challenge Set and ranking first and second for English-Hausa on the Challenge and Evaluation Leaderboards, respectively. We conduct additional experiments on a subset of 250 images, exploring the trade-offs between BLEU scores and semantic similarity across various weighting schemes.
TIM-UNIGE Translation into Low-Resource Languages of Spain for WMT24
Authors: Jonathan Mutal; Lucía Ormaechea
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: We present the results of our constrained submission to the WMT 2024 shared task, which focuses on translating from Spanish into two low-resource languages of Spain: Aranese (spa-arn) and Aragonese (spa-arg). Our system integrates real and synthetic data generated by large language models (e.g., BLOOMZ) and rule-based Apertium translation systems. Built upon the pre-trained NLLB system, our translation model utilizes a multistage approach, progressively refining the initial model through the sequential use of different datasets, starting with large-scale synthetic or crawled data and advancing to smaller, high-quality parallel corpora. This approach resulted in BLEU scores of 30.1 for Spanish to Aranese and 61.9 for Spanish to Aragonese.
TAN-IBE Participation in the Shared Task: Translation into Low-Resource Languages of Spain
Authors: Antoni Oliver
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper describes the systems presented by the TAN-IBE team into the WMT24 Shared task Translation into Low-Resource Languages of Spain. The aim of this joint task was to train systems for Spanish-Asturian, Spanish-Aragonese and Spanish-Aranesian. Our team presented systems for all three language pairs and for two types of submission: for Spanish-Aragonese and Spanish-Aranese we participated with constrained submissions, and for Spanish-Asturian with an open submission.
Enhaced Apertium System: Translation into Low-Resource Languages of Spain Spanish–Asturian
Authors: Sofía García
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: We present the Spanish–Asturian Apertium translation system, which has been enhanced and refined by our team of linguists for the shared task: Low Resource Languages of Spain of this WMT24 under the closed submission. While our system did not rank among the top 10 in terms of results, we believe that Apertium’s translations are of a commendable standard and demonstrate competitiveness with respect to the other systems.
Universitat d’Alacant’s Submission to the WMT 2024 Shared Task on Translation into Low-Resource Languages of Spain
Authors: Aaron Galiano Jimenez; Víctor M. Sánchez-Cartagena; Juan Antonio Perez-Ortiz; Felipe Sánchez-Martínez
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper describes the submissions of the Transducens group of the Universitat d’Alacant to the WMT 2024 Shared Task on Translation into Low-Resource Languages of Spain; in particular, the task focuses on the translation from Spanish into Aragonese, Aranese and Asturian. Our submissions use parallel and monolingual data to fine-tune the NLLB-1.3B model and to investigate the effectiveness of synthetic corpora and transfer-learning between related languages such as Catalan, Galician and Valencian. We also present a many-to-many multilingual neural machine translation model focused on the Romance languages of Spain.
Samsung R&D Institute Philippines @ WMT 2024 Low-resource Languages of Spain Shared Task
Authors: Dan John Velasco; Manuel Antonio Rufino; Jan Christian Blaise Cruz
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper details the submission of Samsung R&D Institute Philippines (SRPH) Language Intelligence Team (LIT) to the WMT 2024 Low-resource Languages of Spain shared task. We trained translation models for Spanish to Aragonese, Spanish to Aranese/Occitan, and Spanish to Asturian using a standard sequence-to-sequence Transformer architecture, augmenting it with a noisy-channel reranking strategy to select better outputs during decoding. For Spanish to Asturian translation, our method reaches comparable BLEU scores to a strong commercial baseline translation system using only constrained data, backtranslations, noisy channel reranking, and a shared vocabulary spanning all four languages.
Back to the Stats: Rescuing Low Resource Neural Machine Translation with Statistical Methods
Authors: Menan Velayuthan; Dilith Jayakody; Nisansa De Silva; Aloka Fernando; Surangika Ranathunga
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: This paper describes our submission to the WMT24 shared task for Low-Resource Languages of Spain in the Constrained task category. Due to the lack of deep learning-based data filtration methods for these languages, we propose a purely statistical-based, two-stage pipeline for data filtration. In the primary stage, we begin by removing spaces and punctuation from the source sentences (Spanish) and deduplicating them. We then filter out sentence pairs with inconsistent language predictions by the language identification model, followed by the removal of pairs with anomalous sentence length and word count ratios, using the development set statistics as the threshold. In the secondary stage, for corpora of significant size, we employ a Jensen Shannon divergence-based method to curate training data of the desired size. Our filtered data allowed us to complete a two-step training process in under 3 hours, with GPU power consumption kept below 1 kWh, making our system both economical and eco-friendly. The source code, training data, and best models are available on the project’s GitHub page.
Hybrid Distillation from RBMT and NMT: Helsinki-NLP’s Submission to the Shared Task on Translation into Low-Resource Languages of Spain
Authors: Ona De Gibert; Mikko Aulamo; Yves Scherrer; Jörg Tiedemann
Venue: EMNLP 2024 (Proceedings of the Ninth Conference on Machine Translation)

Abstract: The Helsinki-NLP team participated in the 2024 Shared Task on Translation into Low-Resource languages of Spain with four multilingual systems covering all language pairs. The task consists in developing Machine Translation (MT) models to translate from Spanish into Aragonese, Aranese and Asturian. Our models leverage known approaches for multilingual MT, namely, data filtering, fine-tuning, data tagging, and distillation. We use distillation to merge the knowledge from neural and rule-based systems and explore the trade-offs between translation quality and computational efficiency. We demonstrate that our distilled models can achieve competitive results while significantly reducing computational costs. Our best models ranked 4th, 5th, and 2nd in the open submission track for Spanish–Aragonese, Spanish–Aranese, and Spanish–Asturian, respectively. We release our code and data publicly at https://github.com/Helsinki-NLP/lowres-spain-st.