4 citations · 4 across the 2 of their papers we have counts for
5 papers · 1 filter
Unsupervised Lexical Simplification with Context Augmentation
Takashi Wada, Timothy Baldwin, Jey Han Lau
We propose a new unsupervised lexical simplification method that uses only monolingual data and pre-trained language models. Given a target word and its context, our method generat…
Unsupervised Paraphrasing of Multiword Expressions
Takashi Wada, Yuji Matsumoto, Timothy Baldwin +1
We propose an unsupervised approach to paraphrasing multiword expressions (MWEs) in context. Our model employs only monolingual corpus data and pre-trained language models (without…
Unsupervised Lexical Substitution with Decontextualised Embeddings
Takashi Wada, Timothy Baldwin, Yuji Matsumoto +1
We propose a new unsupervised method for lexical substitution using pre-trained language models. Compared to previous approaches that use the generative capability of language mode…
Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora
Takashi Wada, Tomoharu Iwata, Yuji Matsumoto +2
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). Our method obtains word em…
Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models
Takashi Wada, Tomoharu Iwata
We propose an unsupervised method to obtain cross-lingual embeddings without any parallel data or pre-trained word embeddings. The proposed model, which we call multilingual neural…