11 citations · 19 across the 13 of their papers we have counts for
13 papers
Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction
Kosuke Yamada, Ryohei Sasano, Koichi Takeda
The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles th…
Sentence Representations via Gaussian Embedding
Shohei Yoda, Hayato Tsukagoshi, Ryohei Sasano +1
Recent progress in sentence embedding, which represents the meaning of a sentence as a point in a vector space, has achieved high performance on tasks such as a semantic textual si…
Semantic Frame Induction with Deep Metric Learning
Kosuke Yamada, Ryohei Sasano, Koichi Takeda
Recent studies have demonstrated the usefulness of contextualized word embeddings in unsupervised semantic frame induction. However, they have also revealed that generic contextual…
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning
Hongkuan Zhang, Saku Sugawara, Akiko Aizawa +3
Image captioning models require the high-level generalization ability to describe the contents of various images in words. Most existing approaches treat the image-caption pairs eq…
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals
Hayato Tsukagoshi, Ryohei Sasano, Koichi Takeda
There have been many successful applications of sentence embedding methods. However, it has not been well understood what properties are captured in the resulting sentence embeddin…
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…
Zero-Shot Translation Quality Estimation with Explicit Cross-Lingual Patterns
Lei Zhou, Liang Ding, Koichi Takeda
This paper describes our submission of the WMT 2020 Shared Task on Sentence Level Direct Assessment, Quality Estimation (QE). In this study, we empirically reveal the \textit{misma…
Pattern-Based Context-Free Grammars for Machine Translation
Koichi Takeda
This paper proposes the use of ``pattern-based'' context-free grammars as a basis for building machine translation (MT) systems, which are now being adopted as personal tools by a…
Tricolor DAGs for Machine Translation
Koichi Takeda
Machine translation (MT) has recently been formulated in terms of constraint-based knowledge representation and unification theories, but it is becoming more and more evident that…