12 citations · 41 across the 16 of their papers we have counts for
18 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…
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition
Takuma Kato, Kaori Abe, Hiroki Ouchi +3
In general, the labels used in sequence labeling consist of different types of elements. For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed int…
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction
Masahiro Kaneko, Masato Mita, Shun Kiyono +2
This paper investigates how to effectively incorporate a pre-trained masked language model (MLM), such as BERT, into an encoder-decoder (EncDec) model for grammatical error correct…
Single Model Ensemble using Pseudo-Tags and Distinct Vectors
Ryosuke Kuwabara, Jun Suzuki, Hideki Nakayama
Model ensemble techniques often increase task performance in neural networks; however, they require increased time, memory, and management effort. In this study, we propose a novel…
Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese
Tatsuki Kuribayashi, Takumi Ito, Jun Suzuki +1
We examine a methodology using neural language models (LMs) for analyzing the word order of language. This LM-based method has the potential to overcome the difficulties existing m…