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This paper describes the submission systems of DLUT-NLP team for the WMT24 low-resource Indic language translation shared task. We participated in the translation task of four language pairs, including en-as, en-mz, en-kha, en-mni.

EMNLP 2024

DLUT-NLP Machine Translation Systems for WMT24 Low-Resource Indic Language Translation

multilingual; pre-training; back-translation;

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Machine Translation for low-resource languages poses significant challenges, primarily due to the limited availability of data.The WMT24 Low-Resource Indic Neural Machine Translation task challenges us to employ innovative techniques to improve machine translation for low-resource Indian languages. Our proposed solution leverages advancements in neural machine translation, focusing on methodologies such as back-translation and fine-tuning. By fine-tuning pretrained models like mBART, we achieved significant progress in translating languages such as Manipuri and Khasi. The best score was achieved for the English-to-Khasi (en-kh) primary model, with the highest BLEU score of 0.0492, chrF score of 0.3316, and METEOR score of 0.2589 (on scale of 0 to 1) and comparable scores for other language pairs.

MTNLP-IIITH: Machine Translation for Low-Resource Indic Languages

Our submission focuses on Assamese-to-English and English-to- Assamese translation, using LLMs as a baseline further fine-tuned with in-domain and synthetic data through various data retrieval strategies. To address low-resource challenges, we incorporated synthetic data generation and few-shot prompting, significantly improving translation accuracy for both language pairs. We present our findings and report on the quality of outputs achieved through these methods.

The SETU-ADAPT Submissions to the WMT24 Low-Resource Indic Language Translation Task

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 finetun- ing of a pre-trained machine translation model, where the pretraining objective includes align- ment of embeddings of tokens from the 22 scheduled Indian languages by a carefully con- structed alignment augmentation strategy (Lin et al., 2020). Our primary system1 is based on language-specific finetuning on this pre-trained model. We achieve chrF2 scores of 50.6, 42.3, 54.9, and 66.3 on the official public test sets for eng→as, eng→kha, eng→lus, eng→mni re- spectively. We also explore multilingual train- ing with/without language grouping and freez- ing of encoder and/or embedding layers.

NLIP_Lab-IITH Low-Resource MT System for WMT24 Indic MT Shared Task

This paper presents our machine translation system that was developed for the WAT2024 MultiIndic 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

System Description of BV-SLP for Sindhi-English Machine Translation in MultiIndic22MT 2024 Shared Task

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 lan- guage families. We explore pre-training for In- dic languages using alignment agreement objec- tives. We utilize bi-lingual dictionaries to sub- stitute words from source sentences. Further- more, we fine-tuned language direction-specific multilingual translation models using small and high-quality seed data. Our primary submis- sion is a 243M parameters multilingual trans- lation model covering 22 Indic languages. In the IN22-Gen benchmark, we achieved an av- erage 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 aver- age 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 model1 is competitive with IndicTransv1 (474M parameter model).

NLIP_Lab-IITH Multilingual MT System for WAT24 MT Shared Task

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 model's performance 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.

English-to-Low-Resource Translation: A Multimodal Approach for Hindi, Malayalam, Bengali, and Hausa

This paper covers the system description of the team “ODIAGEN’s” submission to the 11th Workshop on Asian Translation (WAT 2024). We participated in the English-to-LowRes Multimodal Translation Task, in two of the tasks, i.e. Textonly 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.

OdiaGenAI’s Participation in WMT2024 English-to-Low Resource Multimodal Translation Task

We present our submission to the WMT24 shared task for Low-Resource Languages of Spain in the constrained category. With limited deep learning-based filtration tools for these languages, we developed a purely statistical, two-stage data filtration pipeline. First, we preprocess by removing spaces, punctuation, and duplicates from the Spanish source sentences, followed by filtering based on inconsistent language predictions and sentence-length anomalies. In the second stage, for larger corpora, we employ a Jensen-Shannon divergence method to curate the final training set. Our approach enabled us to train our model in under 3 hours, with GPU consumption below 1 kWh, making it both cost-effective and eco-friendly. Full details, including code and models, are available on our GitHub.

Back to the Stats: Rescuing Low Resource Neural Machine Translation with Statistical Methods

Our work on the WMT24 Shared Task focuses on improving machine translation for low-resource languages of Spain, specifically Aragonese, Asturian, and Aranese. We utilized a distilled NLLB-200 model with added language-specific tokens and a carefully curated data pipeline. Despite limited resources, our TRIBBLE model achieved results close to the Apertium baseline in Asturian, though challenges remain for Aragonese and Aranese.

TRIBBLE - TRanslating IBerian languages Based on Limited E-resources

This paper describes the CloudSheep translation system for WMT24 Discourse-Level Literary Translation shared task. We participated in the Chinese-English direction on the unconstrained track. Our approach to the task used a pipeline of different tools in order to maximize the translation accuracy and flow of the text by combining the strengths of each tool. In particular, our focus was to translate names consistently and idioms correctly. To achieve consistent names throughout a text, a custom name dictionary was generated for each text, containing person and place names, along with their translations. A common honorific dictionary was applied for consistency with titles, especially in historical or cultivation novels. The names were found and translated with GPT 3.5-turbo. To achieve accurate and concise translations of idioms, which are often translated literally and verbosely, we integrated the CC-CEDICT library to provide official definitions. Then, we used GPT-4 to pick the best dictionary definition that fit the context and rephrase it to fit grammatically within a sentence. For the translation of non-name and non-idiom terms, we used Google Translate. We compared our approach's performance with Google Translate as a baseline using BLEU, chrF, and COMET, as well as A/B testing.

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