42 citations · 115 across the 8 of their papers we have counts for
14 papers · 1 filter
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…
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Ikuya Yamada, Akari Asai, Hiroyuki Shindo +2
Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based…
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…
Length-controllable Abstractive Summarization by Guiding with Summary Prototype
Itsumi Saito, Kyosuke Nishida, Kosuke Nishida +5
We propose a new length-controllable abstractive summarization model. Recent state-of-the-art abstractive summarization models based on encoder-decoder models generate only one sum…
Global Entity Disambiguation with BERT
Ikuya Yamada, Koki Washio, Hiroyuki Shindo +1
We propose a global entity disambiguation (ED) model based on BERT. To capture global contextual information for ED, our model treats not only words but also entities as input toke…