12 citations · 24 across the 6 of their papers we have counts for
8 papers · 1 filter
Instance-Based Neural Dependency Parsing
Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi +4
Interpretable rationales for model predictions are crucial in practical applications. We develop neural models that possess an interpretable inference process for dependency parsin…
SHAPE: Shifted Absolute Position Embedding for Transformers
Shun Kiyono, Sosuke Kobayashi, Jun Suzuki +1
Position representation is crucial for building position-aware representations in Transformers. Existing position representations suffer from a lack of generalization to test data…
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi +4
Interpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for struc…
All Word Embeddings from One Embedding
Sho Takase, Sosuke Kobayashi
In neural network-based models for natural language processing (NLP), the largest part of the parameters often consists of word embeddings. Conventional models prepare a large embe…
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions
Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu +2
In this paper, we propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions (e.g., sentences) with a very short learning time, as an alt…
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
Sosuke Kobayashi
We propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are…