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Negative language transfer in learner English: A new dataset ...
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Parallel sentences mining with transfer learning in an unsupervised setting ...
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Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation ...
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Detoxifying Language Models Risks Marginalizing Minority Voices ...
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Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding ...
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Knowledge Enhanced Masked Language Model for Stance Detection ...
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Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model ...
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MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories ...
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DirectProbe: Studying Representations without Classifiers ...
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Challenging distributional models with a conceptual network of philosophical terms ...
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Abstract:
Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.199/ Abstract: Computational linguistic research on language change through distributional semantic (DS) models has inspired researchers from fields such as philosophy and literary studies, who use these methods for the exploration and comparison of comparatively small datasets traditionally analyzed by close reading. Research on methods for small data is still in early stages and it is not clear which methods achieve the best results. We investigate the possibilities and limitations of using distributional semantic models for analyzing philosophical data by means of a realistic use-case. We provide a ground truth for evaluation created by philosophy experts and a blueprint for using DS models in a sound methodological setup. We compare three methods for creating specialized models from small datasets. Though the models do not perform well enough to directly support philosophers yet, we find that models designed for small ...
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Keyword:
Artificial Intelligence; Computer Science and Engineering; Intelligent System; Natural Language Processing
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URL: https://dx.doi.org/10.48448/mzvd-3v98 https://underline.io/lecture/20012-challenging-distributional-models-with-a-conceptual-network-of-philosophical-terms
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ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding ...
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Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems ...
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CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems ...
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multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning ...
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Modeling Framing in Immigration Discourse on Social Media ...
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Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve ...
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SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding ...
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