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    • Eger, Steffen (9)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Constrained Density Matching and Modeling for Cross-lingual Alignment of Contextualized Representations ... Zhao, Wei; Eger, Steffen. - : arXiv, 2022 BASE Show details 2 Towards Explainable Evaluation Metrics for Natural Language Generation ... Leiter, Christoph; Lertvittayakumjorn, Piyawat; Fomicheva, Marina. - : arXiv, 2022 BASE Show details 3 Better than Average: Paired Evaluation of NLP systems ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Eger, Steffen; Peyrard, Maxime. - : Underline Science Inc., 2021 BASE Show details 4 Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors ... Kaster, Marvin; Zhao, Wei; Eger, Steffen. - : arXiv, 2021 BASE Show details 5 Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Eger, Steffen; Kaster, Marvin; Zhao, Wei. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.701/ Abstract: Evaluation metrics are a key ingredient for progress of text generation systems. In recent years, several BERT-based evaluation metrics have been proposed (including BERTScore, MoverScore, BLEURT, etc.) which correlate much better with human assessment of text generation quality than BLEU or ROUGE, invented two decades ago. However, little is known what these metrics, which are based on black-box language model representations, actually capture (it is typically assumed they model semantic similarity). In this work, we use a simple regression based global explainability technique to disentangle metric scores along linguistic factors, including semantics, syntax, morphology, and lexical overlap. We show that the different metrics capture all aspects to some degree, but that they are all substantially sensitive to lexical overlap, just like BLEU and ROUGE. This exposes limitations of these novelly proposed metrics, which we also ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://dx.doi.org/10.48448/aajb-9k90 https://underline.io/lecture/37492-global-explainability-of-bert-based-evaluation-metrics-by-disentangling-along-linguistic-factors BASE Hide details 6 Inducing Language-Agnostic Multilingual Representations ... Zhao, Wei; Eger, Steffen; Bjerva, Johannes. - : arXiv, 2020 BASE Show details 7 On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation ... Zhao, Wei; Glavaš, Goran; Peyrard, Maxime. - : arXiv, 2020 BASE Show details 8 On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation Zhao, Wei; Glavaš, Goran; Peyrard, Maxime. - : Association for Computational Linguistics, 2020 BASE Show details 9 On aligning OpenIE extractions with Knowledge Bases: A case study Gashteovski, Kiril; Gemulla, Rainer; Kotnis, Bhushan. - : Association for Computational Linguistics (ACL), 2020 BASE Show details

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