From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation

• URL: http://arxiv.org/abs/2403.14118v2
• Date: Mon, 28 Oct 2024 07:45:22 GMT
• Title: From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation
• Authors: Haofei Zhao, Yilun Liu, Shimin Tao, Weibin Meng, Yimeng Chen, Xiang Geng, Chang Su, Min Zhang, Hao Yang,
• Abstract summary: Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations. This article provides a comprehensive overview of QE datasets, annotation methods, shared tasks, methodologies, challenges, and future research directions.
• Score: 20.64204462700532
• License:
• Abstract: Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT. After two decades of evolution, QE has yielded a wealth of results. This article provides a comprehensive overview of QE datasets, annotation methods, shared tasks, methodologies, challenges, and future research directions. It begins with an introduction to the background and significance of QE, followed by an explanation of the concepts and evaluation metrics for word-level QE, sentence-level QE, document-level QE, and explainable QE. The paper categorizes the methods developed throughout the history of QE into those based on handcrafted features, deep learning, and Large Language Models (LLMs), with a further division of deep learning-based methods into classic deep learning and those incorporating pre-trained language models (LMs). Additionally, the article details the advantages and limitations of each method and offers a straightforward comparison of different approaches. Finally, the paper discusses the current challenges in QE research and provides an outlook on future research directions.

Related papers

• Benchmarking Large Language Models for Conversational Question Answering in Multi-instructional Documents [61.41316121093604]
  We present InsCoQA, a novel benchmark for evaluating large language models (LLMs) in the context of conversational question answering (CQA) Sourced from extensive, encyclopedia-style instructional content, InsCoQA assesses models on their ability to retrieve, interpret, and accurately summarize procedural guidance from multiple documents. We also propose InsEval, an LLM-assisted evaluator that measures the integrity and accuracy of generated responses and procedural instructions.
  arXiv  Detail & Related papers  (2024-10-01T09:10:00Z)
• FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages [0.0]
  This paper introduces machine-translated versions of FairytaleQA, a renowned QA dataset designed to assess and enhance narrative comprehension skills in young children. We employ fine-tuned, modest-scale models to establish benchmarks for both Question Generation (QG) and QA tasks within the translated datasets. We present a case study proposing a model for generating question-answer pairs, with an evaluation incorporating quality metrics such as question well-formedness, answerability, relevance, and children suitability.
  arXiv  Detail & Related papers  (2024-06-06T16:31:47Z)
• NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens [63.7488938083696]
  NovelQA is a benchmark designed to test the capabilities of Large Language Models with extended texts. This paper presents the design and construction of NovelQA, highlighting its manual annotation, and diverse question types. Our evaluation of Long-context LLMs on NovelQA reveals significant insights into the models' performance.
  arXiv  Detail & Related papers  (2024-03-18T17:32:32Z)
• Document-Level Machine Translation with Large Language Models [91.03359121149595]
  Large language models (LLMs) can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks. This paper provides an in-depth evaluation of LLMs' ability on discourse modeling.
  arXiv  Detail & Related papers  (2023-04-05T03:49:06Z)
• QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization [116.56171113972944]
  We show that carefully choosing the components of a QA-based metric is critical to performance. Our solution improves upon the best-performing entailment-based metric and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-16T00:38:35Z)
• Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation [14.469503103015668]
  We propose a framework to fuse the feature engineering of uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. Experiment results show that our method achieves state-of-the-art performances on the datasets of WMT 2020 QE shared task.
  arXiv  Detail & Related papers  (2021-09-15T08:05:13Z)
• An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers [3.4355075318742165]
  We show that multilingual, word-level QE models perform on par with the current language-specific models. In the cases of zero-shot and few-shot QE, we demonstrate that it is possible to accurately predict word-level quality for any given new language pair from models trained on other language pairs.
  arXiv  Detail & Related papers  (2021-05-31T23:21:10Z)
• Ensemble-based Transfer Learning for Low-resource Machine Translation Quality Estimation [1.7188280334580195]
  We focus on the Sentence-Level QE Shared Task of the Fifth Conference on Machine Translation (WMT20) We propose an ensemble-based predictor-estimator QE model with transfer learning to overcome such QE data scarcity challenge. We achieve the best performance on the ensemble model combining the models pretrained by individual languages as well as different levels of parallel trained corpus with a Pearson's correlation of 0.298.
  arXiv  Detail & Related papers  (2021-05-17T06:02:17Z)
• Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary [65.37544133256499]
  We propose a metric to evaluate the content quality of a summary using question-answering (QA) We demonstrate the experimental benefits of QA-based metrics through an analysis of our proposed metric, QAEval.
  arXiv  Detail & Related papers  (2020-10-01T15:33:09Z)
• Unsupervised Quality Estimation for Neural Machine Translation [63.38918378182266]
  Existing approaches require large amounts of expert annotated data, computation and time for training. We devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. We achieve very good correlation with human judgments of quality, rivalling state-of-the-art supervised QE models.
  arXiv  Detail & Related papers  (2020-05-21T12:38:06Z)

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.