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Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
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BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance
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Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction
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Predicting the Growth of Morphological Families from Social and Linguistic Factors
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Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification
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Relational and Fine-Grained Argument Mining
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In: Datenbank-Spektrum (2020)
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SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
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In: Findings of ACL: EMNLP 2020 (2020)
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Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations
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Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention
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13 |
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context
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Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
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Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking
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A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction
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Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts
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18 |
Analytical Methods for Interpretable Ultradense Word Embeddings
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19 |
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging
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20 |
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
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