ymoslem/acl-abstracts huggingface dataset:
Killing Two Flies with One Stone: An Attempt to Break LLMs Using English-Icelandic Idioms and Proper Names (2024)

The submission of the {\'A}rni Magn{\'u}sson Institute`s team to the WMT24 test suite subtask focuses on idiomatic expressions and proper names for the English{\textrightarrow}Icelandic translation direction. Intuitively and empirically, idioms and proper names are known to be a significant challenge for neural translation models. We create two different test suites. The first evaluates the competency of MT systems in translating common English idiomatic expressions, as well as testing whether systems can distinguish between those expressions and the same phrases when used in a literal context. The second test suite consists of place names that should be translated into their Icelandic exonyms (and correctly inflected) and pairs of Icelandic names that share a surface form between the male and female variants, so that incorrect translations impact meaning as well as readibility. The scores reported are relatively low, especially for idiomatic expressions and place names, and indicate considerable room for improvement.
MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference Calibration (2024)

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 reference-based setting. Furthermore, it achieves comparable results to leading metrics in the reference-free setting, offering greater efficiency.
chrF-S: Semantics Is All You Need (2024)

Machine translation (MT) evaluation metrics like BLEU and chrF++ are widely used reference-based metrics that do not require training and are language-independent. However, these metrics primarily focus on n-gram matching and often overlook semantic depth and contextual understanding. To address this gap, we introduce chrF-S (Semantic 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 embeddings, chrF-S captures both syntactic accuracy and sentence-level semantics. This paper presents our contributions to the WMT24 shared metrics task, showcasing our participation and the development of chrF-S. We also demonstrate that, according to preliminary results on the leaderboard, our metric performs on par with other supervised and LLM-based metrics. By merging semantic insights with n-gram precision, chrF-S offers a significant enhancement in the assessment of machine-generated translations, advancing the field of MT evaluation. Our code and data will be made available at \url{https://github.com/AnanyaCoder/chrF-S}.
MSLC24: Further Challenges for Metrics on a Wide Landscape of Translation Quality (2024)

In this second edition of the Metric Score Landscape Challenge (MSLC), we examine how automatic metrics for machine translation perform on a wide variety of machine translation output, ranging from very low quality systems to the types of high-quality systems submitted to the General MT shared task at WMT. We also explore metric results on specific types of data, such as empty strings, wrong- or mixed-language text, and more. We raise several alarms about inconsistencies in metric scores, some of which can be resolved by increasingly explicit instructions for metric use, while others highlight technical flaws.
MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task (2024)

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.
Evaluating WMT 2024 Metrics Shared Task Submissions on AfriMTE (the African Challenge Set) (2024)

The AfriMTE challenge set from WMT 2024 Metrics Shared Task aims to evaluate the capabilities of evaluation metrics for machine translation on low-resource African languages, which primarily assesses cross-lingual transfer learning and generalization of machine translation metrics across a wide range of under-resourced languages. In this paper, we analyze the submissions to WMT 2024 Metrics Shared Task. Our findings indicate that language-specific adaptation, cross-lingual transfer learning, and larger language model sizes contribute significantly to improved metric performance. Moreover, supervised models with relatively moderate sizes demonstrate robust performance, when augmented with specific language adaptation for low-resource African languages. Finally, submissions show promising results for language pairs including Darija-French, English-Egyptian Arabic, and English-Swahili. However, significant challenges persist for extremely low-resource languages such as English-Luo and English-Twi, highlighting areas for future research and improvement in machine translation metrics for African languages.
Machine Translation Metrics Are Better in Evaluating Linguistic Errors on LLMs than on Encoder-Decoder Systems (2024)

This year`s MT metrics challenge set submission by DFKI expands on previous years' linguistically motivated challenge sets. It includes 137,000 items extracted from 100 MT systems for the two language directions (English to German, English to Russian), covering more than 100 linguistically motivated phenomena organized into 14 linguistic categories. The metrics with the statistically significant best performance in our linguistically motivated analysis are MetricX-24-Hybrid and MetricX-24 for English to German, and MetricX-24 for English to Russian. Metametrics and XCOMET are in the next ranking positions in both language pairs. Metrics are more accurate in detecting linguistic errors in translations by large language models (LLMs) than in translations based on the encoder-decoder neural machine translation (NMT) architecture. Some of the most difficult phenomena for the metrics to score are the transitive past progressive, multiple connectors, and the ditransitive simple future I for English to German, and pseudogapping, contact clauses, and cleft sentences for English to Russian. Despite its overall low performance, the LLM-based metric Gemba performs best in scoring German negation errors.
TMU-HIT`s Submission for the WMT24 Quality Estimation Shared Task: Is GPT-4 a Good Evaluator for Machine Translation? (2024)

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.
HW-TSC 2024 Submission for the Quality Estimation Shared Task (2024)

Quality estimation (QE) is a crucial technique for evaluating the quality of machine translations without the need for reference translations. This paper focuses on Huawei Translation Services Center`s (HW-TSC`s) submission to the sentence-level QE shared task, named LLMs-enhanced-CrossQE. Our system builds upon the CrossQE architecture from our submission from last year, which consists of a multilingual base model and a task-specific downstream layer. The model input is a concatenation of the source and the translated sentences. To enhance performance, we fine-tuned and ensembled multiple base models, including XLM-R, InfoXLM, RemBERT, and CometKiwi. Specifically, we employed two pseudo-data generation methods: 1) a diverse pseudo-data generation method based on the corruption-based data augmentation technique introduced last year, and 2) a pseudo-data generation method that simulates machine translation errors using large language models (LLMs). Our results demonstrate that the system achieves outstanding performance on sentence-level QE test sets.
HW-TSC`s Participation in the WMT 2024 QEAPE Task (2024)

The paper presents the submission by HW-TSC in the WMT 2024 Quality-informed Automatic Post Editing (QEAPE) shared task for the English-Hindi (En-Hi) and English-Tamil (En-Ta) language pair. We use LLM for En-Hi and Transformer for EN-ta respectively. For LLM, we first continue pertrain the Llama3, and then use the real APE data to SFT the pre-trained LLM. As for the transformer in En-Ta, we first pre-train a Machine Translation (MT) model by utilizing MT data collected from the web. Then, we fine-tune the model by employing real APE data.We also use the data augmentation method to enhance our model. Specifically, we incorporate candidate translations obtained from an external Machine Translation (MT) system.Given that APE systems tend to exhibit a tendency of {\textquoteleft}over-correction', we employ a sentence-level Quality Estimation (QE) system to select the final output, deciding between the original translation and the corresponding output generated by the APE model. Our experiments demonstrate that pre-trained MT models are effective when being fine-tuned with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. our approach improves the HTER by -15.99 points and -0.47 points on En-Hi and En-Ta, respectively.
Expanding the FLORES+ Multilingual Benchmark with Translations for Aragonese, Aranese, Asturian, and Valencian (2024)

In this paper, we describe the process of creating the FLORES+ datasets for several Romance languages spoken in Spain, namely Aragonese, Aranese, Asturian, and Valencian. The Aragonese and Aranese datasets are entirely new additions to the FLORES+ multilingual benchmark. An initial version of the Asturian dataset was already available in FLORES+, and our work focused on a thorough revision. Similarly, FLORES+ included a Catalan dataset, which we adapted to the Valencian variety spoken in the Valencian Community. The development of the Aragonese, Aranese, and revised Asturian FLORES+ datasets was undertaken as part of a WMT24 shared task on translation into low-resource languages of Spain.
The Bangla/Bengali Seed Dataset Submission to the WMT24 Open Language Data Initiative Shared Task (2024)

We contribute a seed dataset for the Bangla/Bengali language as part of the WMT24 Open Language Data Initiative shared task. We validate the quality of the dataset against a mined and automatically aligned dataset (NLLBv1) and two other existing datasets of crowdsourced manual translations. The validation is performed by investigating the performance of state-of-the-art translation models fine-tuned on the different datasets after controlling for training set size. Machine translation models fine-tuned on our dataset outperform models tuned on the other datasets in both translation directions (English-Bangla and Bangla-English). These results confirm the quality of our dataset. We hope our dataset will support machine translation for the Bangla/Bengali community and related low-resource languages.
A High-quality Seed Dataset for Italian Machine Translation (2024)

This paper describes the submission of a high-quality translation of the OLDI Seed datasetinto Italian for the WMT 2023 Open LanguageData Initiative shared task.The base of this submission is a previous ver-sion of an Italian OLDI Seed dataset releasedby Haberland et al. (2024) via machine trans-lation and partial post-editing. This data wassubsequently reviewed in its entirety by twonative speakers of Italian, who carried out ex-tensive post-editing with particular attention tothe idiomatic translation of named entities.
Correcting FLORES Evaluation Dataset for Four African Languages (2024)

This paper describes the corrections made to the FLORES evaluation (dev and devtest) dataset for four African languages, namely Hausa, Northern Sotho (Sepedi), Xitsonga, and isiZulu. The original dataset, though groundbreaking in its coverage of low-resource languages, exhibited various inconsistencies and inaccuracies in the reviewed languages that could potentially hinder the integrity of the evaluation of downstream tasks in natural language processing (NLP), especially machine translation. Through a meticulous review process by native speakers, several corrections were identified and implemented, improving the dataset`s overall quality and reliability. For each language, we provide a concise summary of the errors encountered and corrected and also present some statistical analysis that measures the difference between the existing and corrected datasets. We believe that our corrections enhance the linguistic accuracy and reliability of the data and, thereby, contribute to a more effective evaluation of NLP tasks involving the four African languages. Finally, we recommend that future translation efforts, particularly in low-resource languages, prioritize the active involvement of native speakers at every stage of the process to ensure linguistic accuracy and cultural relevance.
Expanding FLORES+ Benchmark for More Low-Resource Settings: Portuguese-Emakhuwa Machine Translation Evaluation (2024)

As part of the Open Language Data Initiative shared tasks, we have expanded the FLORES+ evaluation set to include Emakhuwa, a low-resource language widely spoken in Mozambique. We translated the \textit{dev} and \textit{devtest} sets from Portuguese into Emakhuwa, and we detail the translation process and quality assurance measures used. Our methodology involved various quality checks, including post-editing 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 \url{https://huggingface.co/datasets/LIACC/Emakhuwa-FLORES}
Enhancing Tuvan Language Resources through the FLORES Dataset (2024)

FLORES is a benchmark dataset designed for evaluating machine translation systems, partic- ularly for low-resource languages. This paper, conducted as a part of Open Language Data Ini- tiative (OLDI) shared task, presents our contri- bution to expanding the FLORES dataset with high-quality translations from Russian to Tu- van, an endangered Turkic language. Our ap- proach combined the linguistic expertise of na- tive speakers to ensure both accuracy and cul- tural relevance in the translations. This project represents a significant step forward in support- ing Tuvan as a low-resource language in the realm of natural language processing (NLP) and machine translation (MT).
Machine Translation Evaluation Benchmark for Wu Chinese: Workflow and Analysis (2024)

We introduce a FLORES+ dataset as an evaluation benchmark for modern Wu Chinese machine translation models and showcase its compatibility with existing Wu data. Wu Chinese is mutually unintelligible with other Sinitic languages such as Mandarin and Yue (Cantonese), but uses a set of Hanzi (Chinese characters) that profoundly overlaps with others. The population of Wu speakers is the second largest among languages in China, but the language has been suffering from significant drop in usage especially among the younger generations. We identify Wu Chinese as a textually low-resource language and address challenges for its machine translation models. Our contributions include: (1) an open-source, manually translated dataset, (2) full documentations on the process of dataset creation and validation experiments, (3) preliminary tools for Wu Chinese normalization and segmentation, and (4) benefits and limitations of our dataset, as well as implications to other low-resource languages.
Open Language Data Initiative: Advancing Low-Resource Machine Translation for Karakalpak (2024)

This study presents several contributions for the Karakalpak language: a FLORES+ devtest dataset translated to Karakalpak, parallel corpora for Uzbek-Karakalpak, Russian-Karakalpak and English-Karakalpak of 100,000 pairs each and open-sourced fine-tuned neural models for translation across these languages. Our experiments compare different model variants and training approaches, demonstrating improvements over existing baselines. This work, conducted as part of the Open Language Data Initiative (OLDI) shared task, aims to advance machine translation capabilities for Karakalpak and contribute to expanding linguistic diversity in NLP technologies.
FLORES+ Translation and Machine Translation Evaluation for the Erzya Language (2024)

This paper introduces a translation of the FLORES+ dataset into the endangered Erzya language, with the goal of evaluating machine translation between this language and any of the other 200 languages already included into FLORES+. This translation was carried out as a part of the Open Language Data shared task at WMT24. We also present a benchmark of existing translation models bases on this dataset and a new translation model that achieves the state-of-the-art quality of translation into Erzya from Russian and English.
Spanish Corpus and Provenance with Computer-Aided Translation for the WMT24 OLDI Shared Task (2024)

This paper presents the Seed-CAT submission to the WMT24 Open Language Data Initiative shared task. We detail our data collection method, which involves a computer-aided translation tool developed explicitly for translating Seed corpora. We release a professionally translated Spanish corpus and a provenance dataset documenting the translation process. The quality of the data was validated on the FLORES+ benchmark with English-Spanish neural machine translation models, achieving an average chrF++ score of 34.9.
Efficient Terminology Integration for LLM-based Translation in Specialized Domains (2024)

Traditional machine translation methods typically involve training models directly on large parallel corpora, with limited emphasis on specialized terminology. However, In specialized fields such as patents, finance, biomedical domains, terminology is crucial for translation, with many terminologies that should not be translated based on semantics of the sentence but should be translated following agreed-upon conventions. In this paper we introduce a methodology that efficiently trains models with a smaller amount of data while preserving the accuracy of terminology translation. The terminology extraction model generates a glossary from existing training datasets and further refines the LLM by instructing it to effectively incorporate these terms into translations. We achieve this through a systematic process of term extraction and glossary creation using the Trie Tree algorithm, followed by data reconstruction to teach the LLM how to integrate these specialized terms. This methodology enhances the model`s ability to handle specialized terminology and ensures high-quality translations, particularly in fields where term consistency is crucial. Our approach has demonstrated exceptional performance, achieving the highest translation score among participants in the WMT patent task to date, showcasing its effectiveness and broad applicability in specialized translation domains where general methods often fall short.
Rakuten`s Participation in WMT 2024 Patent Translation Task (2024)

This paper introduces our machine transla- tion system (team sakura), developed for the 2024 WMT Patent Translation Task. Our sys- tem focuses on translations between Japanese- English, Japanese-Korean, and Japanese- Chinese. As large language models have shown good results for various natural language pro- cessing tasks, we have adopted the RakutenAI- 7B-chat model, which has demonstrated effec- tiveness in English and Japanese. We fine-tune this model with patent-domain parallel texts and translate using multiple prompts.
The SETU-ADAPT Submission for WMT 24 Biomedical Shared Task (2024)

This system description paper presents SETU-ADAPT`s submission to the WMT 2024 Biomedical Shared Task, where we participated for the language pairs English-to-French and English-to-German. Our approach focused on fine-tuning Large Language Models, using in-domain and synthetic data, employing different data augmentation and data retrieval strategies. We introduce a novel MT framework, involving three autonomous agents: a Translator Agent, an Evaluator Agent and a Reviewer Agent. We present our findings and report the quality of the outputs.
Findings of WMT 2024 Shared Task on Low-Resource Indic Languages Translation (2024)

This paper presents the results of the low-resource Indic language translation task, organized in conjunction with the Ninth Conference on Machine Translation (WMT) 2024. In this edition, participants were challenged to develop machine translation models for four distinct language pairs: English{--}Assamese, English-Mizo, English-Khasi, and English-Manipuri. The task utilized the enriched IndicNE-Corp1.0 dataset, which includes an extensive collection of parallel and monolingual corpora for northeastern Indic languages. The evaluation was conducted through a comprehensive suite of automatic metrics{---}BLEU, TER, RIBES, METEOR, and ChrF{---}supplemented by meticulous human assessment to measure the translation systems' performance and accuracy. This initiative aims to drive advancements in low-resource machine translation and make a substantial contribution to the growing body of knowledge in this dynamic field.
Findings of WMT 2024`s MultiIndic22MT Shared Task for Machine Translation of 22 Indian Languages (2024)

This paper presents the findings of the WMT 2024`s MultiIndic22MT Shared Task, focusing on Machine Translation (MT) of 22 Indian Languages. In this task, we challenged participants with building MT systems which could translate between any or all of 22 Indian languages in the 8th schedule of the Indian constitution and English. For evaluation, we focused on automatic metrics, namely, chrF, chrF++ and BLEU.
Findings of WMT2024 English-to-Low Resource Multimodal Translation Task (2024)

This paper presents the results of the English-to-Low Resource Multimodal Translation shared tasks from the Ninth Conference on Machine Translation (WMT2024). This year, 7 teams submitted their translation results for the automatic and human evaluation.