Naoaki Okazaki

research

∙ 09/20/2023

Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction

In Grammatical Error Correction (GEC), it is crucial to ensure the user'...

0 Masahiro Kaneko, et al. ∙

research

∙ 09/18/2023

Evaluating Gender Bias of Pre-trained Language Models in Natural Language Inference by Considering All Labels

Discriminatory social biases, including gender biases, have been found i...

0 Panatchakorn Anantaprayoon, et al. ∙

research

∙ 09/16/2023

The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated

Pre-trained language models trained on large-scale data have learned ser...

0 Masahiro Kaneko, et al. ∙

research

∙ 07/21/2023

OUTFOX: LLM-generated Essay Detection through In-context Learning with Adversarially Generated Examples

Large Language Models (LLMs) have achieved human-level fluency in text g...

0 Ryuto Koike, et al. ∙

research

∙ 06/06/2023

SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure Captioning

In scholarly documents, figures provide a straightforward way of communi...

0 Zhishen Yang, et al. ∙

research

∙ 05/29/2023

Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods

Large-scale pre-trained language models such as GPT-3 have shown remarka...

0 Mengsay Loem, et al. ∙

research

∙ 05/19/2023

Reducing Sequence Length by Predicting Edit Operations with Large Language Models

Large Language Models (LLMs) have demonstrated remarkable performance in...

0 Masahiro Kaneko, et al. ∙

research

∙ 05/19/2023

Solving NLP Problems through Human-System Collaboration: A Discussion-based Approach

Humans work together to solve common problems by having discussions, exp...

0 Masahiro Kaneko, et al. ∙

research

∙ 04/22/2023

Semantic Specialization for Knowledge-based Word Sense Disambiguation

A promising approach for knowledge-based Word Sense Disambiguation (WSD)...

0 Sakae Mizuki, et al. ∙

research

∙ 02/17/2023

DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction

Document-level relation extraction (DocRE) is the task of identifying al...

0 Youmi Ma, et al. ∙

research

∙ 01/28/2023

Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples

Numerous types of social biases have been identified in pre-trained lang...

0 Masahiro Kaneko, et al. ∙

research

∙ 11/08/2022

Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency Gaps

IR models using a pretrained language model significantly outperform lex...

0 Hiroki Iida, et al. ∙

research

∙ 10/06/2022

Debiasing isn't enough! – On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Tasks

We study the relationship between task-agnostic intrinsic and task-speci...

4 Masahiro Kaneko, et al. ∙

research

∙ 08/26/2022

Nearest Neighbor Non-autoregressive Text Generation

Non-autoregressive (NAR) models can generate sentences with less computa...

0 Ayana Niwa, et al. ∙

research

∙ 07/27/2022

Are Neighbors Enough? Multi-Head Neural n-gram can be Alternative to Self-attention

Impressive performance of Transformer has been attributed to self-attent...

16 Mengsay Loem, et al. ∙

research

∙ 05/25/2022

PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation

Logical table-to-text generation is a task that involves generating logi...

0 Ao Liu, et al. ∙

research

∙ 05/19/2022

Gender Bias in Meta-Embeddings

Combining multiple source embeddings to create meta-embeddings is consid...

6 Masahiro Kaneko, et al. ∙

research

∙ 03/25/2022

Semi-Supervised Formality Style Transfer with Consistency Training

Formality style transfer (FST) is a task that involves paraphrasing an i...

0 Ao Liu, et al. ∙

research

∙ 03/25/2022

Single Model Ensemble for Subword Regularized Models in Low-Resource Machine Translation

Subword regularizations use multiple subword segmentations during traini...

0 Sho Takase, et al. ∙

research

∙ 03/14/2022

Interpretability for Language Learners Using Example-Based Grammatical Error Correction

Grammatical Error Correction (GEC) should not focus only on high accurac...

6 Masahiro Kaneko, et al. ∙

research

∙ 01/27/2022

Learning How to Translate North Korean through South Korean

South and North Korea both use the Korean language. However, Korean NLP ...

0 Hwichan Kim, et al. ∙

research

∙ 01/14/2022

ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization

Neural models trained with large amount of parallel data have achieved i...

0 Mengsay Loem, et al. ∙

research

∙ 12/12/2021

Improving Logical-Level Natural Language Generation with Topic-Conditioned Data Augmentation and Logical Form Generation

Logical Natural Language Generation, i.e., generating textual descriptio...

6 Ao Liu, et al. ∙

research

∙ 09/15/2021

Transformer-based Lexically Constrained Headline Generation

This paper explores a variant of automatic headline generation methods, ...

0 Kosuke Yamada, et al. ∙

research

∙ 05/26/2021

Joint Optimization of Tokenization and Downstream Model

Since traditional tokenizers are isolated from a downstream task and mod...

0 Tatsuya Hiraoka, et al. ∙

research

∙ 11/30/2020

Multimodal Pretraining Unmasked: Unifying the Vision and Language BERTs

Large-scale pretraining and task-specific fine-tuning is now the standar...

12 Emanuele Bugliarello, et al. ∙

research

∙ 10/15/2020

Named Entity Recognition and Relation Extraction using Enhanced Table Filling by Contextualized Representations

In this study, a novel method for extracting named entities and relation...

0 Youmi Ma, et al. ∙

research

∙ 10/15/2020

Multi-Task Learning for Cross-Lingual Abstractive Summarization

We present a multi-task learning framework for cross-lingual abstractive...

0 Sho Takase, et al. ∙

research

∙ 06/23/2020

Keyframe Segmentation and Positional Encoding for Video-guided Machine Translation Challenge 2020

Video-guided machine translation as one of multimodal neural machine tra...

0 Tosho Hirasawa, et al. ∙

research

∙ 05/05/2020

It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information

The performance of neural machine translation systems is commonly evalua...

0 Emanuele Bugliarello, et al. ∙

research

∙ 05/02/2020

Improving Truthfulness of Headline Generation

Most studies on abstractive summarization re-port ROUGE scores between s...

0 Kazuki Matsumaru, et al. ∙

research

∙ 09/06/2019

Improving Neural Machine Translation with Parent-Scaled Self-Attention

Most neural machine translation (NMT) models operate on source and targe...

0 Emanuele Bugliarello, et al. ∙

research

∙ 07/23/2019

Learning to Select, Track, and Generate for Data-to-Text

We propose a data-to-text generation model with two modules, one for tra...

0 Hayate Iso, et al. ∙

research

∙ 04/16/2019

Positional Encoding to Control Output Sequence Length

Neural encoder-decoder models have been successful in natural language g...

0 Sho Takase, et al. ∙

research

∙ 03/28/2019

A Large-Scale Multi-Length Headline Corpus for Improving Length-Constrained Headline Generation Model Evaluation

Browsing news articles on multiple devices is now possible. The lengths ...

0 Yuta Hitomi, et al. ∙

research

∙ 12/22/2017

Source-side Prediction for Neural Headline Generation

The encoder-decoder model is widely used in natural language generation ...

0 Shun Kiyono, et al. ∙

research

∙ 12/07/2017

A Corpus of Deep Argumentative Structures as an Explanation to Argumentative Relations

In this paper, we compose a new task for deep argumentative structure an...

0 Paul Reisert, et al. ∙

research

∙ 09/06/2017

A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse

This study addresses the problem of identifying the meaning of unknown w...

0 Sosuke Kobayashi, et al. ∙

research

∙ 07/23/2017

Composing Distributed Representations of Relational Patterns

Learning distributed representations for relation instances is a central...

0 Sho Takase, et al. ∙

research

∙ 04/26/2017

Learning Semantically and Additively Compositional Distributional Representations

This paper connects a vector-based composition model to a formal semanti...

0 Ran Tian, et al. ∙

research

∙ 11/26/2015

The Mechanism of Additive Composition

Additive composition (Foltz et al, 1998; Landauer and Dumais, 1997; Mitc...

0 Ran Tian, et al. ∙

Naoaki Okazaki

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