4 citations · 5 across the 5 of their papers we have counts for
11 papers
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…
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi +1
Transformer architecture has become ubiquitous in the natural language processing field. To interpret the Transformer-based models, their attention patterns have been extensively a…
Computationally Efficient Wasserstein Loss for Structured Labels
Ayato Toyokuni, Sho Yokoi, Hisashi Kashima +1
The problem of estimating the probability distribution of labels has been widely studied as a label distribution learning (LDL) problem, whose applications include age estimation,…
Modeling Event Salience in Narratives via Barthes' Cardinal Functions
Takaki Otake, Sho Yokoi, Naoya Inoue +3
Events in a narrative differ in salience: some are more important to the story than others. Estimating event salience is useful for tasks such as story generation, and as a tool fo…
Evaluation of Similarity-based Explanations
Kazuaki Hanawa, Sho Yokoi, Satoshi Hara +1
Explaining the predictions made by complex machine learning models helps users to understand and accept the predicted outputs with confidence. One promising way is to use similarit…
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…