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TINE: A Metric to Assess MT Adequacy.

, , and . WMT@EMNLP, page 116-122. Association for Computational Linguistics, (2011)

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Question Answering by Reasoning Across Documents with Graph Convolutional Networks., , and . CoRR, (2018)Uncertainty in Natural Language Generation: From Theory to Applications., , , , , , , , , and . CoRR, (2023)Examining the Relationship between Preordering and Word Order Freedom in Machine Translation., , , and . WMT, page 118-130. The Association for Computer Linguistics, (2016)Question Answering by Reasoning Across Documents with Graph Convolutional Networks., , and . NAACL-HLT (1), page 2306-2317. Association for Computational Linguistics, (2019)Predicting Machine Translation Adequacy., , , and . MTSummit, (2011)Latent Variable Model for Multi-modal Translation., , and . ACL (1), page 6392-6405. Association for Computational Linguistics, (2019)A Latent Morphology Model for Open-Vocabulary Neural Machine Translation., , and . CoRR, (2019)Latent Transformations for Discrete-Data Normalising Flows., and . CoRR, (2020)Deep Generative Model for Joint Alignment and Word Representation., , and . NAACL-HLT, page 1011-1023. Association for Computational Linguistics, (2018)Vision DiffMask: Faithful Interpretation of Vision Transformers with Differentiable Patch Masking., , , , and . CVPR Workshops, page 3756-3763. IEEE, (2023)