United States

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.

EMNLP 2024

TRIBBLE - TRanslating IBerian languages Based on Limited E-resources

aranese

aragonese

tribble

wmt24 shared task

asturian

nllb-200

low-resource languages

machine translation

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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.

CloudSheep System for WMT24 Discourse-Level Literary Translation

This poster describes our submission system, NovelTrans, from NLP²CT and DeepTranx for the WMT24 Discourse-Level Literary Translation Task in Chinese-English, Chinese-German, and Chinese-Russian language pairs under unconstrained conditions. For our primary system, three translations are done by GPT4o using three different settings of additional information and a terminology table generated by online models. The final result is composed of sentences that have the highest xCOMET score compared with the corresponding sentences in other results. Our system achieved an xCOMET score of 79.14 which is higher than performing a direct chapter-level translation on our dataset.

NovelTrans: System for WMT24 Discourse-Level Literary Translation

The rapid growth of deep learning has spurred significant advancements across industries, particularly in machine translation through large languagemodels (LLMs). However, translating literary still presents challenges, including cross-cultural nuances, complex language structures, metaphoricalexpressions, and cultural differences. To address these issues, this study utilizes the Llama and Phi models using both LoRA and full-parametertechniques, along-side a prompt-based translation system.

LinChance×NTU for Unconstrained WMT2024 Literary Translation

Chat text translation in multinational customer service faces unique challenges such as colloquial expressions, abbreviations, and context-dependent meanings. This study proposes a context-aware sliding window mechanism to enhance translation quality. Through ablation studies, we compare multilingual and bilingual fine-tuning approaches and evaluate the impact of window sizes on translation performance. Our findings demonstrate how contextual information, fine-tuning strategies, and window size influence document-level translation quality, ensuring consistent context handling and robust cross-linguistic performance.

Optimising LLM-Driven Machine Translation with Context-Aware Sliding Windows

Translating conversational text, particularly in customer support contexts, presents unique challenges due to its informal and unstructured nature. We propose a context-aware LLM translation system that leverages conversation summarization and dialogue history to enhance translation quality for the English-Korean language pair. Our approach incorporates the two most recent dialogues as raw data and a summary of earlier conversations to manage context length effectively. We demonstrate that this method significantly improves translation accuracy, maintaining coherence and consistency across conversations. This system offers a practical solution for customer support translation tasks, addressing the complexities of conversational text.

Context-Aware LLM Translation System Using Conversation Summarization and Dialogue History

For this shared task, we have used several ma- chine translation engines to produce transla- tions (en ⇔ fr) by fine-tuning a dialog-oriented NMT engine and having NMT baseline trans- lations post-edited with prompt engineering. Our objectives are to test the effectiveness of a fine-tuning strategy with help of a robust NMT model, to draw out a from-translation-to-post- editing pipeline, and to evaluate the strong and weak points of NMT systems.

Enhancing Translation Quality: A Comparative Study of Fine-Tuning and Prompt Engineering in Dialog-Oriented Machine Translation Systems. Insights from the MULTITAN-GML Team

Our approach focused on fine-tuning Large Language Models (LLMs), using in- domain and synthetic data, employing different data retrieval strategies. We built our MT systems using multilingual LLMs such as NLLB-200-600M, Llama-3-8b and mBART-50. We present our findings and report the quality of the outputs.

The SETU-ADAPT Submissions to WMT 2024 Chat Translation Tasks

While Minimum Bayes Risk (MBR) decoding using metrics such as COMET or MetricX has outperformed traditional decoding methods such as greedy or beam search, it introduces a challenge we refer to as metric bias. As MBR decoding aims to produce translations that score highly according to a specific utility metric, this very process makes it impossible to use the same metric for both decoding and evaluation, as improvements might simply be due to reward hacking rather than reflecting real quality improvements. In this work we find that compared to human ratings, neural metrics not only overestimate the quality of MBR decoding when the same metric is used as the utility metric, but they also overestimate the quality of MBR/QE decoding with other neural utility metrics as well. We also show that the metric bias issue can be mitigated by using an ensemble of utility metrics during MBR decoding: human evaluations show that MBR decoding using an ensemble of utility metrics outperforms a single utility metric.

Mitigating Metric Bias in Minimum Bayes Risk Decoding

Collecting high-quality translations is crucial for the development and evaluation of machine translation systems. However, traditional human-only approaches are costly and slow. This study presents a comprehensive investigation of 11 approaches for acquiring translation data, including human-only, machineonly, and hybrid approaches. Our findings demonstrate that human-machine collaboration can match or even exceed the quality of human-only translations, while being more cost-efficient. Error analysis reveals the complementary strengths between human and machine contributions, highlighting the effectiveness of collaborative methods. Cost analysis further demonstrates the economic benefits of human-machine collaboration methods, with some approaches achieving top-tier quality at around 60% of the cost of traditional methods. We release a publicly available dataset1 containing nearly 18,000 segments of varying translation quality with corresponding human ratings to facilitate future research.

Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data

Machine translation (MT) of user-generated content (UGC) poses unique challenges, including handling slang, emotion, and literary devices like irony and sarcasm. Evaluating the quality of these translations is challenging as current metrics do not focus on these ubiquitous features of UGC. To address this issue, we utilize an existing emotion-related dataset that includes emotion labels and human-annotated translation errors based on Multi-dimensional Quality Metrics. We extend it with sentence-level evaluation scores and word-level labels, leading to a dataset suitable for sentence- and word-level translation evaluation and emotion classification, in a multi-task setting. We propose a new architecture to perform these tasks concurrently, with a novel combined loss function, which integrates different loss heuristics, like the Nash and Aligned losses. Our evaluation compares existing fine-tuning and multi-task learning approaches, assessing generalization with ablative experiments over multiple datasets. Our approach achieves state-of-the-art performance and we present a comprehensive analysis for MT evaluation of UGC.

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Next from EMNLP 2024

CloudSheep System for WMT24 Discourse-Level Literary Translation

Similar lecture

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