Search Results for author: Terufumi Morishita

Found 16 papers, 7 papers with code

appjsonify: An Academic Paper PDF-to-JSON Conversion Toolkit

1 code implementation2 Oct 2023 Atsuki Yamaguchi, Terufumi Morishita

We present appjsonify, a Python-based PDF-to-JSON conversion toolkit for academic papers.

Document Layout Analysis

Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic

1 code implementation11 Aug 2023 Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro Sogawa

We rethink this and adopt a well-grounded set of deduction rules based on formal logic theory, which can derive any other deduction rules when combined in a multistep way.

Formal Logic Logical Reasoning

LARCH: Large Language Model-based Automatic Readme Creation with Heuristics

1 code implementation6 Aug 2023 Yuta Koreeda, Terufumi Morishita, Osamu Imaichi, Yasuhiro Sogawa

Writing a readme is a crucial aspect of software development as it plays a vital role in managing and reusing program code.

Language Modelling Large Language Model

How do different tokenizers perform on downstream tasks in scriptio continua languages?: A case study in Japanese

1 code implementation16 Jun 2023 Takuro Fujii, Koki Shibata, Atsuki Yamaguchi, Terufumi Morishita, Yasuhiro Sogawa

This paper investigates the effect of tokenizers on the downstream performance of pretrained language models (PLMs) in scriptio continua languages where no explicit spaces exist between words, using Japanese as a case study.

How does the task complexity of masked pretraining objectives affect downstream performance?

1 code implementation18 May 2023 Atsuki Yamaguchi, Hiroaki Ozaki, Terufumi Morishita, Gaku Morio, Yasuhiro Sogawa

Masked language modeling (MLM) is a widely used self-supervised pretraining objective, where a model needs to predict an original token that is replaced with a mask given contexts.

Language Modelling Masked Language Modeling

Controlling keywords and their positions in text generation

1 code implementation19 Apr 2023 Yuichi Sasazawa, Terufumi Morishita, Hiroaki Ozaki, Osamu Imaichi, Yasuhiro Sogawa

In this paper, we tackle a novel task of controlling not only keywords but also the position of each keyword in the text generation.

Position Story Generation

Rethinking Fano's Inequality in Ensemble Learning

1 code implementation25 May 2022 Terufumi Morishita, Gaku Morio, Shota Horiguchi, Hiroaki Ozaki, Nobuo Nukaga

We propose a fundamental theory on ensemble learning that answers the central question: what factors make an ensemble system good or bad?

Ensemble Learning

Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity

no code implementations SEMEVAL 2020 Terufumi Morishita, Gaku Morio, Hiroaki Ozaki, Toshinori Miyoshi

Due to the unsupervised nature of the task, we concentrated on inquiring about the similarity measures induced by different layers of different pre-trained Transformer-based language models, which can be good approximations of the human sense of word similarity.

Word Similarity

Hitachi at SemEval-2020 Task 7: Stacking at Scale with Heterogeneous Language Models for Humor Recognition

no code implementations SEMEVAL 2020 Terufumi Morishita, Gaku Morio, Hiroaki Ozaki, Toshinori Miyoshi

Our experimental results show that SaS outperforms a naive average ensemble, leveraging weaker PLMs as well as high-performing PLMs.

Hitachi at MRP 2020: Text-to-Graph-Notation Transducer

no code implementations CONLL 2020 Hiroaki Ozaki, Gaku Morio, Yuta Koreeda, Terufumi Morishita, Toshinori Miyoshi

This paper presents our proposed parser for the shared task on Meaning Representation Parsing (MRP 2020) at CoNLL, where participant systems were required to parse five types of graphs in different languages.

Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization

no code implementations ACL 2020 Gaku Morio, Hiroaki Ozaki, Terufumi Morishita, Yuta Koreeda, Kohsuke Yanai

Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges.

Argument Mining

Cannot find the paper you are looking for? You can Submit a new open access paper.