COMET: A Neural Framework for MT Evaluation

• URL: http://arxiv.org/abs/2009.09025v2
• Date: Mon, 19 Oct 2020 14:10:10 GMT
• Title: COMET: A Neural Framework for MT Evaluation
• Authors: Ricardo Rei, Craig Stewart, Ana C Farinha, Alon Lavie
• Abstract summary: We present COMET, a neural framework for training multilingual machine translation evaluation models. Our framework exploits information from both the source input and a target-language reference translation in order to more accurately predict MT quality. Our models achieve new state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.
• Score: 8.736370689844682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art levels of correlation with human judgements. Our framework leverages recent breakthroughs in cross-lingual pretrained language modeling resulting in highly multilingual and adaptable MT evaluation models that exploit information from both the source input and a target-language reference translation in order to more accurately predict MT quality. To showcase our framework, we train three models with different types of human judgements: Direct Assessments, Human-mediated Translation Edit Rate and Multidimensional Quality Metrics. Our models achieve new state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.

Related papers

• Towards Zero-Shot Multimodal Machine Translation [64.9141931372384]
  We propose a method to bypass the need for fully supervised data to train multimodal machine translation systems. Our method, called ZeroMMT, consists in adapting a strong text-only machine translation (MT) model by training it on a mixture of two objectives. To prove that our method generalizes to languages with no fully supervised training data available, we extend the CoMMuTE evaluation dataset to three new languages: Arabic, Russian and Chinese.
  arXiv  Detail & Related papers  (2024-07-18T15:20:31Z)
• Revisiting Machine Translation for Cross-lingual Classification [91.43729067874503]
  Most research in the area focuses on the multilingual models rather than the Machine Translation component. We show that, by using a stronger MT system and mitigating the mismatch between training on original text and running inference on machine translated text, translate-test can do substantially better than previously assumed.
  arXiv  Detail & Related papers  (2023-05-23T16:56:10Z)
• Unified Model Learning for Various Neural Machine Translation [63.320005222549646]
  Existing machine translation (NMT) studies mainly focus on developing dataset-specific models. We propose a versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks. OurNMT results in substantial improvements over dataset-specific models with significantly reduced model deployment costs.
  arXiv  Detail & Related papers  (2023-05-04T12:21:52Z)
• Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning [3.2258463207097017]
  Generative Adversarial Networks (GAN) offer a promising approach for Neural Machine Translation (NMT) In GAN, similar to bilingual models, multilingual NMT only considers one reference translation for each sentence during model training. This article proposes Denoising Adversarial Auto-encoder-based Sentence Interpolation (DAASI) approach to perform sentence computation.
  arXiv  Detail & Related papers  (2023-03-31T12:34:14Z)
• Evaluating and Improving the Coreference Capabilities of Machine Translation Models [30.60934078720647]
  Machine translation requires a wide range of linguistic capabilities. Current end-to-end models are expected to learn implicitly by observing aligned sentences in bilingual corpora.
  arXiv  Detail & Related papers  (2023-02-16T18:16:09Z)
• Pre-training Data Quality and Quantity for a Low-Resource Language: New Corpus and BERT Models for Maltese [4.4681678689625715]
  We analyse the effect of pre-training with monolingual data for a low-resource language. We present a newly created corpus for Maltese, and determine the effect that the pre-training data size and domain have on the downstream performance. We compare two models on the new corpus: a monolingual BERT model trained from scratch (BERTu), and a further pre-trained multilingual BERT (mBERTu)
  arXiv  Detail & Related papers  (2022-05-21T06:44:59Z)
• Data Selection Curriculum for Neural Machine Translation [31.55953464971441]
  We introduce a two-stage curriculum training framework for NMT models. We fine-tune a base NMT model on subsets of data, selected by both deterministic scoring using pre-trained methods and online scoring. We have shown that our curriculum strategies consistently demonstrate better quality (up to +2.2 BLEU improvement) and faster convergence.
  arXiv  Detail & Related papers  (2022-03-25T19:08:30Z)
• Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT [64.1841519527504]
  neural machine translation uses a single neural network to model the entire translation process. Despite neural machine translation being de-facto standard, it is still not clear how NMT models acquire different competences over the course of training.
  arXiv  Detail & Related papers  (2021-09-03T09:38:50Z)
• Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model [58.27176041092891]
  Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements. We propose a novel unsupervised feature decomposition method that can automatically extract domain-specific features from the entangled pretrained cross-lingual representations. Our proposed model leverages mutual information estimation to decompose the representations computed by a cross-lingual model into domain-invariant and domain-specific parts.
  arXiv  Detail & Related papers  (2020-11-23T16:00:42Z)
• Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation [59.191079800436114]
  Document-level machine translation incorporates inter-sentential dependencies into the translation of a source sentence. We propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.
  arXiv  Detail & Related papers  (2020-03-30T03:38:01Z)

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.