Search Results for author:
Found 9 papers, 5 papers with code
A Fashion Item Recommendation Model in Hyperbolic Space
In this work, we propose a fashion item recommendation model that incorporates hyperbolic geometry into user and item representations.
Unsupervised Lexical Simplification with Context Augmentation
We propose a new unsupervised lexical simplification method that uses only monolingual data and pre-trained language models.
Unsupervised Paraphrasing of Multiword Expressions
We propose an unsupervised approach to paraphrasing multiword expressions (MWEs) in context.
Unsupervised Lexical Substitution with Decontextualised Embeddings
We propose a new unsupervised method for lexical substitution using pre-trained language models.
Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation
We propose a simple method for nominal coordination boundary identification.
Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora
We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e. g. a few hundred sentence pairs).
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+5
Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models
Recently, a variety of unsupervised methods have been proposed that map pre-trained word embeddings of different languages into the same space without any parallel data.
Bayesian Optimization for Multi-objective Optimization and Multi-point Search
It is difficult to analytically maximize the acquisition function as the computational cost is prohibitive even when approximate calculations such as sampling approximation are performed; therefore, we propose an accurate and computationally efficient method for estimating gradient of the acquisition function, and develop an algorithm for Bayesian optimization with multi-objective and multi-point search.
Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models
The proposed model contains bidirectional LSTMs that perform as forward and backward language models, and these networks are shared among all the languages.
