United States

This paper describes the development process of NMT systems that were submitted to the WMT~2024 General Translation and Biomedical shared tasks by the team of AIST AIRC. At WMT 2024 AIST AIRC participated in the General Machine Translation shared task and the Biomedical Translation task. We trained constrained track models for translation between English, German, and Japanese. Before training our models, we first filtered the parallel data, then performed iterative back-translation and additional filtering. We experimented with training baseline Transformer models, Mega models, and fine-tuning open-source T5 and Gemma model checkpoints using the filtered parallel data. Our primary submissions contain translations from ensembles of two Mega model checkpoints and our contrastive submissions are generated by our fine-tuned T5 model checkpoints.

EMNLP 2024

AIST AIRC Systems for the WMT 2024 Shared Tasks

machine translation; shared task; biomedical domain

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This poster describes the submission of the very first version of the Occiglot open-source large language model to the General MT Shared Task of the 9th Conference of Machine Translation (WMT24). Occiglot is an open-source, community-based LLM based on Mistral-7B, which went through language-specific continual pre-training and subsequent instruction tuning, including instructions relevant to machine translation. We examine the automatic metric scores for translating the WMT24 test set and provide a detailed linguistically-motivated analysis. Despite Occiglot performing worse than many of the other system submissions, we observe that it performs better than Mistral7B, which has been based upon, which indicates the positive effect of the language specific continual-pretraining and instruction tuning. We see the submission of this very early version of the model as a motivation to unite community forces and pursue future LLM research on the translation task.

Occiglot at WMT24: European Open-source Large Language Models Evaluated on Translation

This paper presents an evaluation of 16 machine translation systems submitted to the Shared Task of the 9th Conference of Machine Translation (WMT24) for the English-Hindi (en-hi) language pair using our Complex Structures Test (CoST) suite. Aligning with this year’s test suite sub- task theme, “Help us break LLMs”, we curated a comprehensive test suite encompassing diverse datasets across various categories, including autobiography, poetry, legal, conversation, play, narration, technical, and mixed genres. Our evaluation reveals that all the systems struggle significantly with the archaic style of text like legal and technical writings or text with creative twist like conversation and poetry datasets, highlighting their weaknesses in handling complex linguistic structures and stylistic nuances inherent in these text types. Our evaluation identifies the strengths and limitations of the submitted models, pointing to specific areas where further research and development are needed to enhance their performance.

CoST of breaking the LLMs

We assess the difficulty of gender resolution in literary-style dialogue settings and the influence of gender stereotypes. Instances of the test suite contain spoken dialogue interleaved with external meta-context about the characters and the manner of speaking. We find that character and manner stereotypes outside of the dialogue significantly impact the gender agreement of referents within the dialogue.

WMT24 Test Suite: Gender Resolution in Speaker-Listener Dialogue Roles

The GenderQueer Test Suite is designed to probe MT systems’ capabilities and limitations in handling gender agreement in morphologically rich languages such as Icelandic as well as non-binary pronouns and LGBTQIA+ terminology.

The GenderQueer Test Suite

This paper summarizes the results of our test suite evaluation on 39 machine translation systems submitted at the Shared Task of the Ninth Conference of Machine Translation (WMT24). It offers a fine-grained linguistic evaluation of machine translation outputs for English– German and English–Russian, resulting from a significant manual linguistic effort. Based on our results, LLMs are inferior to NMT in English–German, both in overall scores and when translating specific linguistic phenomena, such as punctuation, complex future verb tenses, and stripping. LLMs show quite a competitive performance in English-Russian, although top-performing systems might struggle with some cases of named entities and terminology, function words, mediopassive voice, and semantic roles. Additionally, some LLMs generate very verbose or empty outputs, posing challenges to the evaluation process.

Investigating the Linguistic Performance of Large Language Models in Machine Translation

We present MetaMetrics-MT, an innovative metric designed to evaluate machine translation (MT) tasks by aligning closely with human preferences through Bayesian optimization with Gaussian Processes. MetaMetrics-MT enhances existing MT metrics by optimizing their correlation with human judgments. Our experiments on the WMT24 metric shared task dataset demonstrate that MetaMetrics-MT outperforms all existing baselines, setting a new benchmark for state-of-the-art performance in the referencebased setting. Furthermore, it achieves comparable results to leading metrics in the referencefree setting, offering greater efficiency.

MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference Calibration

Machine translation (MT) evaluation metrics like BLEU and chrF++ are widely used reference-based metrics that do not require training and are language-independent. How- ever, these metrics primarily focus on n- gram matching and often overlook semantic depth and contextual understanding. To ad- dress this gap, we introduce chrF-S (Seman- tic chrF++), an enhanced metric that integrates sentence embeddings to evaluate translation quality more comprehensively. By combining traditional character and word n-gram analysis with semantic information derived from em- beddings, chrF-S captures both syntactic ac- curacy and sentence-level semantics. This pa- per presents our contributions to the WMT24 shared metrics task, showcasing our partici- pation and the development of chrF-S. We also demonstrate that, according to prelimi- nary results on the leaderboard, our metric performs on par with other supervised and LLM-based metrics. By merging semantic in- sights with n-gram precision, chrF-S offers a significant enhancement in the assessment of machine-generated translations, advancing the field of MT evaluation.

chrF-S: Semantics Is All You Need

In this paper, we present the MetricX-24 submissions to the WMT24 Metrics Shared Task and provide details on the improvements we made over the previous version of MetricX. Our primary submission is a hybrid reference-based/-free metric, which can score a translation irrespective of whether it is given the source segment, the reference, or both. The metric is trained on previous WMT data in a two-stage fashion, first on the DA ratings only, then on a mixture of MQM and DA ratings. The training set in both stages is augmented with synthetic examples that we created to make the metric more robust to several common failure modes, such as fluent but unrelated translation, or undertranslation. We demonstrate the benefits of the individual modifications via an ablation study, and show a significant performance increase over MetricX-23 on the WMT23 MQM ratings, as well as our new synthetic challenge set.

MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task

In machine translation quality estimation (QE), translation quality is evaluated automatically without the need for reference translations. This paper describes our contribution to the sentence-level subtask of Task 1 at the Ninth Machine Translation Conference (WMT24), which predicts quality scores for neural MT outputs without reference translations. We fine-tune GPT-4o mini, a large-scale language model (LLM), with limited data for QE. We report results for the direct assessment (DA) method for four language pairs: English-Gujarati (En-Gu), English-Hindi (En-Hi), English-Tamil (En-Ta), and English-Telugu (En-Te). Experiments under zero-shot, few-shot prompting, and fine-tuning settings revealed significantly low performance in the zero-shot, while fine-tuning achieved accuracy comparable to last year’s best scores. Our system demonstrated the effectiveness of this approach in low-resource language QE, securing 1st place in both En-Gu and En-Hi, and 4th place in En-Ta and En-Te. The code used in our experiments is available at the following URL (https://colab.research.google.com/drive/1p8VMnAkRfuVpbvM_revV2ZaN76sSxmiE?usp=sharing)..

TMU-HIT’s Submission for the WMT24 Quality Estimation Shared Task: Is GPT-4 a Good Evaluator for Machine Translation?

As part of the Open Language Data Initiative shared tasks, we have expanded the FLORES+ evaluation set to include Emakhuwa, a lowresource language widely spoken in Mozambique. We translated the dev and devtest sets from Portuguese into Emakhuwa, and we detail the translation process and quality assurance measures used. Our methodology involved various quality checks, including postediting and adequacy assessments. The resulting datasets consist of multiple reference sentences for each source. We present baseline results from training a Neural Machine Translation system and fine-tuning existing multilingual translation models. Our findings suggest that spelling inconsistencies remain a challenge in Emakhuwa. Additionally, the baseline models underperformed on this evaluation set, underscoring the necessity for further research to enhance machine translation quality for Emakhuwa. The data is publicly available at https://huggingface.co/ datasets/LIACC/Emakhuwa-FLORES

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