3 citations · 6 across the 5 of their papers we have counts for
6 papers
Transformer-based Lexically Constrained Headline Generation
Kosuke Yamada, Yuta Hitomi, Hideaki Tamori +4
This paper explores a variant of automatic headline generation methods, where a generated headline is required to include a given phrase such as a company or a product name. Previo…
DefSent: Sentence Embeddings using Definition Sentences
Hayato Tsukagoshi, Ryohei Sasano, Koichi Takeda
Sentence embedding methods using natural language inference (NLI) datasets have been successfully applied to various tasks. However, these methods are only available for limited la…
Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering
Kosuke Yamada, Ryohei Sasano, Koichi Takeda
Recent studies on semantic frame induction show that relatively high performance has been achieved by using clustering-based methods with contextualized word embeddings. However, t…
Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction
Kosuke Yamada, Ryohei Sasano, Koichi Takeda
Contextualized word representations have proven useful for various natural language processing tasks. However, it remains unclear to what extent these representations can cover han…
Self-Guided Curriculum Learning for Neural Machine Translation
Lei Zhou, Liang Ding, Kevin Duh +3
In the field of machine learning, the well-trained model is assumed to be able to recover the training labels, i.e. the synthetic labels predicted by the model should be as close t…
Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition
Thai-Hoang Pham, Khai Mai, Nguyen Minh Trung +4
Fine-Grained Named Entity Recognition (FG-NER) is critical for many NLP applications. While classical named entity recognition (NER) has attracted a substantial amount of research,…