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Integrating Vectorized Lexical Constraints for Neural Machine Translation ...
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Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction
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In: Mathematics; Volume 10; Issue 8; Pages: 1344 (2022)
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Virtual Reality-Integrated Immersion-Based Teaching to English Language Learning Outcome
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In: Front Psychol (2022)
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Alternated Training with Synthetic and Authentic Data for Neural Machine Translation ...
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CPM-2: Large-scale Cost-effective Pre-trained Language Models ...
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VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator ...
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Assessing Multilingual Fairness in Pre-trained Multimodal Representations ...
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Dialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset ...
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Transfer Learning for Sequence Generation: from Single-source to Multi-source ...
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Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision ...
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Learning to Selectively Learn for Weakly-supervised Paraphrase Generation ...
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SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection ...
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Analyzing the Limits of Self-Supervision in Handling Bias in Language ...
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Statistically significant detection of semantic shifts using contextual word embeddings ...
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SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection ...
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Statistically Significant Detection of Semantic Shifts using Contextual Word Embeddings ...
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Abstract:
Detecting lexical semantic change in smaller data sets, e.g. in historical linguistics and digital humanities, is challenging due to a lack of statistical power. This issue is exacerbated by non-contextual embedding models that produce one embedding per word and, therefore, mask the variability present in the data. In this article, we propose an approach to estimate semantic shift by combining contextual word embeddings with permutation-based statistical tests. We use the false discovery rate procedure to address the large number of hypothesis tests being conducted simultaneously. We demonstrate the performance of this approach in simulation where it achieves consistently high precision by suppressing false positives. We additionally analyze real-world data from SemEval-2020 Task 1 and the Liverpool FC subreddit corpus. We show that by taking sample variation into account, we can improve the robustness of individual semantic shift estimates without degrading overall performance. ...
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Keyword:
Computational Linguistics; Machine Learning; Natural Language Processing
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URL: https://underline.io/lecture/39283-statistically-significant-detection-of-semantic-shifts-using-contextual-word-embeddings https://dx.doi.org/10.48448/4qhk-jd49
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Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation ...
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SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection ...
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