Search Results for author: Fei Cheng

Found 45 papers, 16 papers with code

Improving Event Duration Question Answering by Leveraging Existing Temporal Information Extraction Data

1 code implementation LREC 2022 Felix Virgo, Fei Cheng, Sadao Kurohashi

However, the amount of training data for tasks like duration question answering, i. e., McTACO, is very limited, suggesting a need for external duration information to improve this task.

Question Answering Temporal Information Extraction

JaMIE: A Pipeline Japanese Medical Information Extraction System with Novel Relation Annotation

1 code implementation LREC 2022 Fei Cheng, Shuntaro Yada, Ribeka Tanaka, Eiji Aramaki, Sadao Kurohashi

In this paper, we first propose a novel relation annotation schema for investigating the medical and temporal relations between medical entities in Japanese medical reports.

Relation Relation Extraction

Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis

no code implementations9 Oct 2024 Shiho Matta, Yin Jou Huang, Fei Cheng, Hirokazu Kiyomaru, Yugo Murawaki

Recent studies have demonstrated that few-shot learning allows LLMs to generate training data for supervised models at a low cost.

Few-Shot Learning

Beyond English-Centric LLMs: What Language Do Multilingual Language Models Think in?

no code implementations20 Aug 2024 Chengzhi Zhong, Fei Cheng, Qianying Liu, Junfeng Jiang, Zhen Wan, Chenhui Chu, Yugo Murawaki, Sadao Kurohashi

We examine the latent language of three typical categories of models for Japanese processing: Llama2, an English-centric model; Swallow, an English-centric model with continued pre-training in Japanese; and LLM-jp, a model pre-trained on balanced English and Japanese corpora.

Efficient Continual Learning with Low Memory Footprint For Edge Device

no code implementations15 Jul 2024 Zeqing Wang, Fei Cheng, Kangye Ji, Bohu Huang

Different from other CL methods bringing huge resource consumption to acquire generalizability among all tasks for delaying forgetting, LightCL compress the resource consumption of already generalized components in neural networks and uses a few extra resources to improve memory in other parts.

Continual Learning Recommendation Systems

LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs

no code implementations4 Jul 2024 LLM-jp, :, Akiko Aizawa, Eiji Aramaki, Bowen Chen, Fei Cheng, Hiroyuki Deguchi, Rintaro Enomoto, Kazuki Fujii, Kensuke Fukumoto, Takuya Fukushima, Namgi Han, Yuto Harada, Chikara Hashimoto, Tatsuya Hiraoka, Shohei Hisada, Sosuke Hosokawa, Lu Jie, Keisuke Kamata, Teruhito Kanazawa, Hiroki Kanezashi, Hiroshi Kataoka, Satoru Katsumata, Daisuke Kawahara, Seiya Kawano, Atsushi Keyaki, Keisuke Kiryu, Hirokazu Kiyomaru, Takashi Kodama, Takahiro Kubo, Yohei Kuga, Ryoma Kumon, Shuhei Kurita, Sadao Kurohashi, Conglong Li, Taiki Maekawa, Hiroshi Matsuda, Yusuke Miyao, Kentaro Mizuki, Sakae Mizuki, Yugo Murawaki, Ryo Nakamura, Taishi Nakamura, Kouta Nakayama, Tomoka Nakazato, Takuro Niitsuma, Jiro Nishitoba, Yusuke Oda, Hayato Ogawa, Takumi Okamoto, Naoaki Okazaki, Yohei Oseki, Shintaro Ozaki, Koki Ryu, Rafal Rzepka, Keisuke Sakaguchi, Shota Sasaki, Satoshi Sekine, Kohei Suda, Saku Sugawara, Issa Sugiura, Hiroaki Sugiyama, Hisami Suzuki, Jun Suzuki, Toyotaro Suzumura, Kensuke Tachibana, Yu Takagi, Kyosuke Takami, Koichi Takeda, Masashi Takeshita, Masahiro Tanaka, Kenjiro Taura, Arseny Tolmachev, Nobuhiro Ueda, Zhen Wan, Shuntaro Yada, Sakiko Yahata, Yuya Yamamoto, Yusuke Yamauchi, Hitomi Yanaka, Rio Yokota, Koichiro Yoshino

This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs).

AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction

no code implementations14 Jun 2024 Peitao Han, Lis Kanashiro Pereira, Fei Cheng, Wan Jou She, Eiji Aramaki

Existing in-context learning (ICL) methods for relation extraction (RE) often prioritize language similarity over structural similarity, which can lead to overlooking entity relationships.

In-Context Learning Relation +3

Generalized W-Net: Arbitrary-style Chinese Character Synthesization

no code implementations10 Jun 2024 Haochuan Jiang, Guanyu Yang, Fei Cheng, Kaizhu Huang

Synthesizing Chinese characters with consistent style using few stylized examples is challenging.

Jump-teaching: Ultra Efficient and Robust Learning with Noisy Label

no code implementations27 May 2024 Kangye Ji, Fei Cheng, Zeqing Wang, Bohu Huang

Therefore, we employ only one network with the jump manner update to decouple the interplay and mine more semantic information from the loss for a more precise selection.

Learning with noisy labels Selection bias

AcTED: Automatic Acquisition of Typical Event Duration for Semi-supervised Temporal Commonsense QA

no code implementations27 Mar 2024 Felix Virgo, Fei Cheng, Lis Kanashiro Pereira, Masayuki Asahara, Ichiro Kobayashi, Sadao Kurohashi

We propose a voting-driven semi-supervised approach to automatically acquire the typical duration of an event and use it as pseudo-labeled data.

Potential Societal Biases of ChatGPT in Higher Education: A Scoping Review

no code implementations24 Nov 2023 Ming Li, Ariunaa Enkhtur, Beverley Anne Yamamoto, Fei Cheng, Lilan Chen

We categorized the potential societal biases that GAI might cause in the field of higher education.

Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain

1 code implementation5 Oct 2023 Zhen Wan, Yating Zhang, Yexiang Wang, Fei Cheng, Sadao Kurohashi

In the zero-shot setting of four Chinese legal tasks, our method improves accuracy by 33. 3\% compared to the direct generation by GPT-4.

Domain Adaptation

Pushing the Limits of ChatGPT on NLP Tasks

no code implementations16 Jun 2023 Xiaofei Sun, Linfeng Dong, Xiaoya Li, Zhen Wan, Shuhe Wang, Tianwei Zhang, Jiwei Li, Fei Cheng, Lingjuan Lyu, Fei Wu, Guoyin Wang

In this work, we propose a collection of general modules to address these issues, in an attempt to push the limits of ChatGPT on NLP tasks.

Dependency Parsing Event Extraction +9

MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting

1 code implementation26 May 2023 Tatsuro Inaba, Hirokazu Kiyomaru, Fei Cheng, Sadao Kurohashi

Large language models (LLMs) have achieved impressive performance on various reasoning tasks.

Task 2

Comprehensive Solution Program Centric Pretraining for Table-and-Text Hybrid Numerical Reasoning

no code implementations12 May 2023 Qianying Liu, Dongsheng Yang, Wenjie Zhong, Fei Cheng, Sadao Kurohashi

Numerical reasoning over table-and-text hybrid passages, such as financial reports, poses significant challenges and has numerous potential applications.

GPT-RE: In-context Learning for Relation Extraction using Large Language Models

1 code implementation3 May 2023 Zhen Wan, Fei Cheng, Zhuoyuan Mao, Qianying Liu, Haiyue Song, Jiwei Li, Sadao Kurohashi

In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e. g., GPT-3), they still lag significantly behind fully-supervised baselines (e. g., fine-tuned BERT) in relation extraction (RE).

In-Context Learning Relation +2

Textual Enhanced Contrastive Learning for Solving Math Word Problems

1 code implementation29 Nov 2022 Yibin Shen, Qianying Liu, Zhuoyuan Mao, Fei Cheng, Sadao Kurohashi

Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information.

Contrastive Learning Math

ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision

no code implementations13 Oct 2022 Qianying Liu, Wenyu Guan, Jianhao Shen, Fei Cheng, Sadao Kurohashi

To address this problem, we propose a novel search algorithm with combinatorial strategy \textbf{ComSearch}, which can compress the search space by excluding mathematically equivalent equations.

Math

Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems

1 code implementation21 Sep 2022 Yibin Shen, Qianying Liu, Zhuoyuan Mao, Zhen Wan, Fei Cheng, Sadao Kurohashi

To solve Math Word Problems, human students leverage diverse reasoning logic that reaches different possible equation solutions.

Math

Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision

no code implementations18 May 2022 Zhen Wan, Fei Cheng, Qianying Liu, Zhuoyuan Mao, Haiyue Song, Sadao Kurohashi

Contrastive pre-training on distant supervision has shown remarkable effectiveness in improving supervised relation extraction tasks.

Contrastive Learning Relation +1

Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding

no code implementations10 Nov 2021 Qianying Liu, Fei Cheng, Sadao Kurohashi

Meta learning with auxiliary languages has demonstrated promising improvements for cross-lingual natural language processing.

Cross-Lingual Transfer Meta-Learning +3

JaMIE: A Pipeline Japanese Medical Information Extraction System

1 code implementation8 Nov 2021 Fei Cheng, Shuntaro Yada, Ribeka Tanaka, Eiji Aramaki, Sadao Kurohashi

We present an open-access natural language processing toolkit for Japanese medical information extraction.

ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation

no code implementations17 Mar 2021 Shitian He, Huanxin Zou, Yingqian Wang, Runlin Li, Fei Cheng

In this paper, we explore the potential benefits introduced by image SR to ship detection, and propose an end-to-end network named ShipSRDet.

Image Super-Resolution

A Hybrid Bandit Framework for Diversified Recommendation

no code implementations24 Dec 2020 Qinxu Ding, Yong liu, Chunyan Miao, Fei Cheng, Haihong Tang

Previous interactive recommendation methods primarily focus on learning users' personalized preferences on the relevance properties of an item set.

Diversity Recommendation Systems

Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction

1 code implementation Findings of the Association for Computational Linguistics 2020 Ranran Haoran Zhang, Qianying Liu, Aysa Xuemo Fan, Heng Ji, Daojian Zeng, Fei Cheng, Daisuke Kawahara, Sadao Kurohashi

We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets.

Joint Entity and Relation Extraction Relation +1

Predicting Event Time by Classifying Sub-Level Temporal Relations Induced from a Unified Representation of Time Anchors

no code implementations14 Aug 2020 Fei Cheng, Yusuke Miyao

Another contribution of this work is to construct a larger event time corpus (256 news documents) with a reasonable Inter-Annotator Agreement (IAA), for the purpose of overcoming the data shortage of the existing event time corpus (36 news documents).

Multi-Label Classification

Pre-training via Leveraging Assisting Languages and Data Selection for Neural Machine Translation

no code implementations23 Jan 2020 Haiyue Song, Raj Dabre, Zhuoyuan Mao, Fei Cheng, Sadao Kurohashi, Eiichiro Sumita

To this end, we propose to exploit monolingual corpora of other languages to complement the scarcity of monolingual corpora for the LOI.

Machine Translation NMT +1

Random Occlusion-recovery for Person Re-identification

no code implementations26 Sep 2018 Di Wu, Kun Zhang, Fei Cheng, Yang Zhao, Qi Liu, Chang-An Yuan, De-Shuang Huang

As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping multiple cameras or across different time with a single camera.

Generative Adversarial Network Person Re-Identification

Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths

no code implementations ACL 2017 Fei Cheng, Yusuke Miyao

In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (Bi-LSTM) along dependency paths (DP).

General Classification Question Answering +4

Parsing Chinese Synthetic Words with a Character-based Dependency Model

no code implementations LREC 2014 Fei Cheng, Kevin Duh, Yuji Matsumoto

Synthetic word analysis is a potentially important but relatively unexplored problem in Chinese natural language processing.

Chinese Word Segmentation Segmentation

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