Multilingual Coreference Resolution via Cycle-Consistent Machine Translation

• URL: http://arxiv.org/abs/2606.05444v1
• Date: Wed, 03 Jun 2026 21:06:55 GMT
• Title: Multilingual Coreference Resolution via Cycle-Consistent Machine Translation
• Abstract summary: Coreference resolution is a core NLP task, having a broad range of downstream applications, e.g.machine translation, question answering, document summarization, etc.<n>We propose a novel coreference resolution pipeline that harnesses machine translation (MT) from English to a target low-resource language.<n>Experiments on four low-resource languages show that our pipeline brings significant performance gains in coreference resolution.
• Score: 20.187814511700243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coreference resolution is a core NLP task, having a broad range of downstream applications, e.g.~machine translation, question answering, document summarization, etc. While the task is well-studied in English, comparatively less attention is dedicated to coreference resolution in other languages, especially low-resource ones. To mitigate this gap, we propose a novel coreference resolution pipeline that harnesses machine translation (MT) from English to a target low-resource language, to generate or expand training data. To automatically validate the quality of the translated samples, we back-translate the samples and assess the similarity with the original English samples via cosine similarity in the latent space of a BERT model. The resulting similarity scores are integrated into the loss function to weight training samples according to their MT cycle consistency. Extensive experiments on four low-resource languages show that our pipeline brings significant performance gains in coreference resolution. Moreover, our pipeline enables accurate coreference resolution in languages where no previous corpora were available.

Related papers

• A Komi-Yazva--Russian Parallel Corpus and Evaluation Protocol for Zero- and Few-Shot LLM Translation [0.0]
  We present the first Komi-Yazva--Russian parallel corpus together with an explicit evaluation protocol.<n>The dataset contains 457 aligned sentence pairs from 74 narrative texts.<n>We use this setup to compare modern large language models on Komi-Yazva-to-Russian translation under severe parallel-data scarcity.
  arXiv  Detail & Related papers  (2026-06-04T17:26:38Z)
• Evaluating Extremely Low-Resource Machine Translation: A Comparative Study of ChrF++ and BLEU Metrics [69.2321983942375]
  This work presents a comparative analysis of BLEU, an n-gram-based metric, and ChrF++, a character-based metric, for MT evaluation in ELRL settings.<n>We examine how each metric responds to translation artifacts, including hallucinations, repetition, source-text copying, and diacritic (textitmatra) variations across three ELRLs: Magahi, Bhojpuri, and Chhattisgarhi.<n>While recent work often relies solely on ChrF++, our findings show that BLEU, despite its lower absolute scores, provides complementary lexical-precision insights that improve interpretability.
  arXiv  Detail & Related papers  (2026-02-19T14:56:42Z)
• Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation [49.82863380286994]
  In-context learning may offer novel ways to adapt Large Language Models for low-resource machine translation.<n>In this study, we explore scaling low-resource machine translation ICL beyond the few-shot setting to thousands of examples with long-context models.<n>Our experiments on Javanese and Sundanese show that gains from additional context saturate quickly and can degrade near the maximum context window.
  arXiv  Detail & Related papers  (2026-02-04T17:02:22Z)
• The Saturation Point of Backtranslation in High Quality Low Resource English Gujarati Machine Translation [0.0]
  Backtranslation BT is widely used in low resource machine translation MT to generate additional synthetic training data using monolingual corpora.<n>We explore the effectiveness of backtranslation for English Gujarati translation using the multilingual pretrained MBART50 model.
  arXiv  Detail & Related papers  (2025-06-12T09:02:53Z)
• Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu [53.437954702561065]
  In-context machine translation (MT) with large language models (LLMs) is a promising approach for low-resource MT.<n>This study systematically investigates how each type of resource, e.g., dictionary, grammar book, and retrieved parallel examples, affect the translation performance.<n>Our results indicate that high-quality dictionaries and good parallel examples are very helpful, while grammars hardly help.
  arXiv  Detail & Related papers  (2025-02-17T14:53:49Z)
• A Data Selection Approach for Enhancing Low Resource Machine Translation Using Cross-Lingual Sentence Representations [0.4499833362998489]
  This study focuses on the case of English-Marathi language pairs, where existing datasets are notably noisy. To mitigate the impact of data quality issues, we propose a data filtering approach based on cross-lingual sentence representations. Results demonstrate a significant improvement in translation quality over the baseline post-filtering with IndicSBERT.
  arXiv  Detail & Related papers  (2024-09-04T13:49:45Z)
• Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study [1.6819960041696331]
  In this paper, we revisit state-of-the-art Neural Machine Translation techniques to develop automatic translation systems between German and Bavarian. Our experiment entails applying Back-translation and Transfer Learning to automatically generate more training data and achieve higher translation performance. Statistical significance results with Bonferroni correction show surprisingly high baseline systems, and that Back-translation leads to significant improvement.
  arXiv  Detail & Related papers  (2024-04-12T06:16:26Z)
• Strategies for improving low resource speech to text translation relying on pre-trained ASR models [59.90106959717875]
  This paper presents techniques and findings for improving the performance of low-resource speech to text translation (ST) We conducted experiments on both simulated and real-low resource setups, on language pairs English - Portuguese, and Tamasheq - French respectively.
  arXiv  Detail & Related papers  (2023-05-31T21:58:07Z)
• Rethinking Round-Trip Translation for Machine Translation Evaluation [44.83568796515321]
  We report the surprising finding that round-trip translation can be used for automatic evaluation without the references. We demonstrate the rectification is overdue as round-trip translation could benefit multiple machine translation evaluation tasks.
  arXiv  Detail & Related papers  (2022-09-15T15:06:20Z)
• Improving Multilingual Translation by Representation and Gradient Regularization [82.42760103045083]
  We propose a joint approach to regularize NMT models at both representation-level and gradient-level. Our results demonstrate that our approach is highly effective in both reducing off-target translation occurrences and improving zero-shot translation performance.
  arXiv  Detail & Related papers  (2021-09-10T10:52:21Z)
• Modelling Latent Translations for Cross-Lingual Transfer [47.61502999819699]
  We propose a new technique that integrates both steps of the traditional pipeline (translation and classification) into a single model. We evaluate our novel latent translation-based model on a series of multilingual NLU tasks. We report gains for both zero-shot and few-shot learning setups, up to 2.7 accuracy points on average.
  arXiv  Detail & Related papers  (2021-07-23T17:11:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.