| Challenge: | Existing methods to predict interpreter confidence and the adequacy of the interpreted message are lacking. |
| Approach: | They propose to extend a QE pipeline to estimate interpreter performance by using five settings in three language pairs. |
| Outcome: | The proposed method can predict interpreter confidence and adequacy over five settings in three language pairs and improves interpretation strategy and evaluation measures. |
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Are we Estimating or Guesstimating Translation Quality? (2020.acl-main)
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| Challenge: | A carefully engineered ensemble of pre-trained multilingual language models won the QE shared task at WMT19. |
| Approach: | They propose to use pre-trained multilingual language models to train quality estimation for machine translation. |
| Outcome: | A carefully engineered ensemble of pre-trained language models wins the QE shared task at WMT19. |
Barriers to Effective Evaluation of Simultaneous Interpretation (2024.findings-eacl)
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| Challenge: | Existing studies have relied on out-of-the-box machine translation metrics to evaluate interpretation data, but they do not account for human judgments of interpretation quality. |
| Approach: | They propose to use machine translation metrics to evaluate human interpretations to address potential barriers to disfluency, summarization, paraphrasing and segmentation. |
| Outcome: | The proposed model achieves better correlation with human judgments than state-of-the-art metrics. |
Translation Quality Estimation by Jointly Learning to Score and Rank (2020.emnlp-main)
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| Challenge: | The translation quality estimation (QE) task aims to evaluate the general quality of a translation without using reference translations. |
| Approach: | They propose a translation quality estimation task that uses translations as reference . they propose supervised learning using cross-lingual sentence embeddings from pre-trained multilingual models. |
| Outcome: | The proposed model outperforms sentBLEU on the WMT 2019 QE as a Metric task and outperformed sentBLUE on the QE in a multilingual language task. |
Multimodal Quality Estimation for Machine Translation (2020.acl-main)
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| Challenge: | Existing work has only explored textual context. |
| Approach: | They propose to use visual and text modalities to explore Quality Estimation for Machine Translation and integrate them into multimodal QE frameworks. |
| Outcome: | The proposed approaches improve on sentence-level and document-level predictions using visual features extracted from images. |
SpeechQE: Estimating the Quality of Direct Speech Translation (2024.emnlp-main)
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| Challenge: | Recent advances in automatic quality estimation for machine translation focus on written language, leaving the speech modality underexplored. |
| Approach: | They propose a new quality estimation system based on cascaded and end-to-end architectures. |
| Outcome: | The proposed system is better suited to estimating the quality of direct speech translation than existing systems designed for text translation. |
Simultaneous Translation (2020.emnlp-tutorials)
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| Challenge: | Simultaneous translation is a problem that has long been considered one of the hardest problems in AI . this tutorial will provide a deep understanding of the history and the recent advances in simultaneous translation. |
| Approach: | This tutorial will examine the design and evaluation of policies for simultaneous translation . it will provide an overview of the history and recent advances in simultaneous translation. |
| Outcome: | This tutorial will examine the design and evaluation of policies for simultaneous translation . |
Self-Supervised Quality Estimation for Machine Translation (2021.emnlp-main)
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Yuanhang Zheng, Zhixing Tan, Meng Zhang, Mieradilijiang Maimaiti, Huanbo Luan, Maosong Sun, Qun Liu, Yang Liu
| Challenge: | Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain. |
| Approach: | They propose a self-supervised method to evaluate machine-translated sentences without references by recovering masked target words. |
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SIMULEVAL: An Evaluation Toolkit for Simultaneous Translation (2020.emnlp-demos)
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| Challenge: | SimulEval is an evaluation toolkit for simultaneous text and speech translation. |
| Approach: | They propose a server-client scheme for simultaneous translation that uses server input and client policies to evaluate models. |
| Outcome: | The proposed evaluation toolkit is available for both text and speech translation. |
Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation (N19-1)
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| Challenge: | Experimental results on a newly-annotated version of the NAIST Simultaneous Translation Corpus indicate the promise of our proposed method. |
| Approach: | They propose a task of predicting which terminology simultaneous interpreters will leave untranslated using supervised sequence taggers. |
| Outcome: | The proposed method predicts which terminology interpreters leave untranslated . it is based on an annotated version of the NAIST Simultaneous Translation Corpus . |
An Exploratory Study on Multilingual Quality Estimation (2020.aacl-main)
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Shuo Sun, Marina Fomicheva, Frédéric Blain, Vishrav Chaudhary, Ahmed El-Kishky, Adithya Renduchintala, Francisco Guzmán, Lucia Specia
| Challenge: | Existing approaches to predict the quality of machine translation use language-specific models, but they lack labelled data for each language pair. |
| Approach: | They propose to use scores from translation models to estimate quality of machine translations by predicting the quality of a translation at test time. |
| Outcome: | The proposed models outperform single-language models in less balanced quality label distributions and low-resource settings. |