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Found 7 papers, 0 papers with code
Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages
Cross-lingual word embeddings provide a way for information to be transferred between languages.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+1
UNBNLP at SemEval-2021 Task 1: Predicting lexical complexity with masked language models and character-level encoders
In this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1.
Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora
In this paper, we propose a novel method for learning cross-lingual word embeddings, that incorporates sub-word information during training, and is able to learn high-quality embeddings from modest amounts of monolingual data and a bilingual lexicon.
Evaluating Sub-word Embeddings in Cross-lingual Models
This is particularly problematic for low-resource and morphologically-rich languages, which often have relatively high OOV rates.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+1
UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language
In this paper we apply a range of approaches to language modeling {--} including word-level n-gram and neural language models, and character-level neural language models {--} to the problem of detecting hate speech and offensive language.
Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?
In this paper, we propose the first model for multiword expression (MWE) compositionality prediction based on character-level neural network language models.
UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes
In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency.
