no code implementations • NAACL (SocialNLP) 2021 • Hayato Kobayashi, Hiroaki Taguchi, Yoshimune Tabuchi, Chahine Koleejan, Ken Kobayashi, Soichiro Fujita, Kazuma Murao, Takeshi Masuyama, Taichi Yatsuka, Manabu Okumura, Satoshi Sekine
Ranking the user comments posted on a news article is important for online news services because comment visibility directly affects the user experience.
no code implementations • EMNLP 2021 • Jingun Kwon, Naoki Kobayashi, Hidetaka Kamigaito, Manabu Okumura
Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents.
Ranked #4 on Extractive Text Summarization on CNN / Daily Mail
no code implementations • RANLP 2021 • Ying Zhang, Hidetaka Kamigaito, Tatsuya Aoki, Hiroya Takamura, Manabu Okumura
Encoder-decoder models have been commonly used for many tasks such as machine translation and response generation.
no code implementations • RANLP 2021 • Jingyi You, Chenlong Hu, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
Neural sequence-to-sequence (Seq2Seq) models and BERT have achieved substantial improvements in abstractive document summarization (ADS) without and with pre-training, respectively.
no code implementations • RANLP 2021 • Jingun Kwon, Naoki Kobayashi, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
The results demonstrate that the position of emojis in texts is a good clue to boost the performance of emoji label prediction.
no code implementations • RANLP 2021 • Yukun Feng, Chenlong Hu, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
Character-aware neural language models can capture the relationship between words by exploiting character-level information and are particularly effective for languages with rich morphology.
1 code implementation • RANLP 2021 • Thodsaporn Chay-intr, Hidetaka Kamigaito, Manabu Okumura
These models estimate word boundaries from a character sequence.
Ranked #2 on Thai Word Segmentation on BEST-2010
1 code implementation • ECCV 2020 • Soichiro Fujita, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata
This paper proposes a new evaluation framework, Story Oriented Dense video cAptioning evaluation framework (SODA), for measuring the performance of video story description systems.
no code implementations • EMNLP 2021 • Ying Zhang, Hidetaka Kamigaito, Manabu Okumura
Discourse segmentation and sentence-level discourse parsing play important roles for various NLP tasks to consider textual coherence.
no code implementations • COLING 2022 • Jingyi You, Dongyuan Li, Manabu Okumura, Kenji Suzuki
Automated radiology report generation aims to generate paragraphs that describe fine-grained visual differences among cases, especially those between the normal and the diseased.
no code implementations • COLING 2022 • Dongyuan Li, Jingyi You, Kotaro Funakoshi, Manabu Okumura
Text infilling aims to restore incomplete texts by filling in blanks, which has attracted more attention recently because of its wide application in ancient text restoration and text rewriting.
no code implementations • NAACL 2022 • Jingyi You, Dongyuan Li, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura
Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them.
1 code implementation • 2 May 2024 • Shiyin Tan, Dongyuan Li, Renhe Jiang, Ying Zhang, Manabu Okumura
Graph augmentation has received great attention in recent years for graph contrastive learning (GCL) to learn well-generalized node/graph representations.
no code implementations • 1 May 2024 • Dongyuan Li, Zhen Wang, Yankai Chen, Renhe Jiang, Weiping Ding, Manabu Okumura
Active learning seeks to achieve strong performance with fewer training samples.
1 code implementation • 1 May 2024 • Dongyuan Li, Ying Zhang, Yusong Wang, Funakoshi Kataro, Manabu Okumura
To address these issues, we propose an active learning (AL)-based fine-tuning framework for SER, called \textsc{After}, that leverages task adaptation pre-training (TAPT) and AL methods to enhance performance and efficiency.
1 code implementation • 30 Mar 2024 • Aru Maekawa, Satoshi Kosugi, Kotaro Funakoshi, Manabu Okumura
To address this issue, we propose a novel text dataset distillation approach, called Distilling dataset into Language Model (DiLM), which trains a language model to generate informative synthetic training samples as text data, instead of directly optimizing synthetic samples.
1 code implementation • 8 Mar 2024 • Aru Maekawa, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura
Recently, decoder-only pre-trained large language models (LLMs), with several tens of billion parameters, have significantly impacted a wide range of natural language processing (NLP) tasks.
no code implementations • 19 Feb 2024 • Jian Wu, Linyi Yang, Manabu Okumura, Yue Zhang
Although Large Language Models (LLMs) have shown strong performance in Multi-hop Question Answering (MHQA) tasks, their real reasoning ability remains exploration.
no code implementations • 17 Feb 2024 • Jian Wu, Linyi Yang, Yuliang Ji, Wenhao Huang, Börje F. Karlsson, Manabu Okumura
Multi-hop QA (MHQA) involves step-by-step reasoning to answer complex questions and find multiple relevant supporting facts.
no code implementations • 18 Nov 2023 • Dongyuan Li, Yusong Wang, Kotaro Funakoshi, Manabu Okumura
In this paper, we propose a method for joint modality fusion and graph contrastive learning for multimodal emotion recognition (Joyful), where multimodality fusion, contrastive learning, and emotion recognition are jointly optimized.
no code implementations • 14 Nov 2023 • Yuhan Li, Jian Wu, Zhiwei Yu, Börje F. Karlsson, Wei Shen, Manabu Okumura, Chin-Yew Lin
To close this gap in data availability and enable cross-modality IE, while alleviating labeling costs, we propose a semi-supervised pipeline for annotating entities in text, as well as entities and relations in tables, in an iterative procedure.
no code implementations • 30 Sep 2023 • Dongyuan Li, Yusong Wang, Kotaro Funakoshi, Manabu Okumura
However, existing SER methods ignore the information gap between the pre-training speech recognition task and the downstream SER task, leading to sub-optimal performance.
no code implementations • 22 Sep 2023 • Zifan Wang, Kotaro Funakoshi, Manabu Okumura
This work proposes PMAN (Prompting-based Metric on ANswerability), a novel automatic evaluation metric to assess whether the generated questions are answerable by the reference answers for the QG tasks.
2 code implementations • Journal of Natural Language Processing 2023 • Thodsaporn Chay-intr, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura
Our model employs the lattice structure to handle segmentation alternatives and utilizes graph neural networks along with an attention mechanism to attentively extract multi-granularity representation from the lattice for complementing character representations.
Ranked #1 on Chinese Word Segmentation on CTB6 (using extra training data)
no code implementations • 1 Jun 2023 • Congda Ma, Tianyu Zhao, Makoto Shing, Kei Sawada, Manabu Okumura
In a controllable text generation dataset, there exist unannotated attributes that could provide irrelevant learning signals to models that use it for training and thus degrade their performance.
no code implementations • 24 May 2023 • Jian Wu, Yicheng Xu, Yan Gao, Jian-Guang Lou, Börje F. Karlsson, Manabu Okumura
A common challenge in HQA and other passage-table QA datasets is that it is generally unrealistic to iterate over all table rows, columns, and linked passages to retrieve evidence.
1 code implementation • 22 May 2023 • Ying Zhang, Hidetaka Kamigaito, Manabu Okumura
Pre-trained seq2seq models have achieved state-of-the-art results in the grammatical error correction task.
1 code implementation • 15 Oct 2022 • Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata
To promote and further develop RST-style discourse parsing models, we need a strong baseline that can be regarded as a reference for reporting reliable experimental results.
Ranked #1 on Discourse Parsing on Instructional-DT (Instr-DT)
1 code implementation • COLING 2022 • Yidong Wang, Hao Wu, Ao Liu, Wenxin Hou, Zhen Wu, Jindong Wang, Takahiro Shinozaki, Manabu Okumura, Yue Zhang
Limited labeled data increase the risk of distribution shift between test data and training data.
1 code implementation • NAACL 2022 • Toshiki Kawamoto, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura
A repetition is a response that repeats words in the previous speaker's utterance in a dialogue.
no code implementations • NAACL (ACL) 2022 • Soichiro Murakami, Peinan Zhang, Sho Hoshino, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
Writing an ad text that attracts people and persuades them to click or act is essential for the success of search engine advertising.
2 code implementations • NeurIPS 2021 • BoWen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, Takahiro Shinozaki
However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to consider different learning status and learning difficulties of different classes.
1 code implementation • ACL 2021 • Lya Hulliyyatus Suadaa, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura, Hiroya Takamura
In summary, our contributions are (1) a new dataset for numerical table-to-text generation using pairs of a table and a paragraph of a table description with richer inference from scientific papers, and (2) a table-to-text generation framework enriched with numerical reasoning.
no code implementations • NAACL 2021 • Hidetaka Kamigaito, Peinan Zhang, Hiroya Takamura, Manabu Okumura
Although there are many studies on neural language generation (NLG), few trials are put into the real world, especially in the advertising domain.
no code implementations • NAACL 2021 • Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata
We then pre-train a neural RST parser with the obtained silver data and fine-tune it on the RST-DT.
Ranked #2 on Discourse Parsing on RST-DT (using extra training data)
no code implementations • EACL 2021 • Hidetaka Kamigaito, Jingun Kwon, Young-In Song, Manabu Okumura
We therefore propose a method for extracting interesting relationships between persons from natural language texts by focusing on their surprisingness.
no code implementations • EACL 2021 • Chenlong Hu, Yukun Feng, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
This work presents multi-modal deep SVDD (mSVDD) for one-class text classification.
1 code implementation • EACL 2021 • Soichiro Murakami, Sora Tanaka, Masatsugu Hangyo, Hidetaka Kamigaito, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura
The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users.
no code implementations • EACL 2021 • Lya Hulliyyatus Suadaa, Hidetaka Kamigaito, Manabu Okumura, Hiroya Takamura
Numerical tables are widely used to present experimental results in scientific papers.
no code implementations • COLING 2020 • Jingun Kwon, Hidetaka Kamigaito, Young-In Song, Manabu Okumura
Recently, automatic trivia fact extraction has attracted much research interest.
1 code implementation • Asian Chapter of the Association for Computational Linguistics 2020 • Yukun Feng, Chenlong Hu, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
We propose a simple and effective method for incorporating word clusters into the Continuous Bag-of-Words (CBOW) model.
no code implementations • COLING 2020 • Shogo Fujita, Tomohide Shibata, Manabu Okumura
In community-based question answering (CQA) platforms, it takes time for a user to get useful information from among many answers.
no code implementations • COLING 2020 • Shogo Fujita, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
We tackle the task of automatically generating a function name from source code.
no code implementations • COLING 2020 • Riku Kawamura, Tatsuya Aoki, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
We propose neural models that can normalize text by considering the similarities of word strings and sounds.
1 code implementation • 3 Apr 2020 • Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata
To obtain better discourse dependency trees, we need to improve the accuracy of RST trees at the upper parts of the structures.
Ranked #3 on Discourse Parsing on RST-DT
1 code implementation • 4 Feb 2020 • Hidetaka Kamigaito, Manabu Okumura
Sentence compression is the task of compressing a long sentence into a short one by deleting redundant words.
Ranked #1 on Sentence Compression on Google Dataset
no code implementations • WS 2019 • Takumi Ohtani, Hidetaka Kamigaito, Masaaki Nagata, Manabu Okumura
We present neural machine translation models for translating a sentence in a text by using a graph-based encoder which can consider coreference relations provided within the text explicitly.
no code implementations • IJCNLP 2019 • Naoki Kobayashi, Tsutomu Hirao, Kengo Nakamura, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata
The first one builds the optimal tree in terms of a dissimilarity score function that is defined for splitting a text span into smaller ones.
no code implementations • CONLL 2019 • Yukun Feng, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
Our injection method can also be used together with previous methods.
no code implementations • RANLP 2019 • Tatsuya Ishigaki, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
To incorporate the information of a discourse tree structure into the neural network-based summarizers, we propose a discourse-aware neural extractive summarizer which can explicitly take into account the discourse dependency tree structure of the source document.
no code implementations • ACL 2019 • Soichiro Fujita, Hayato Kobayashi, Manabu Okumura
Ranking comments on an online news service is a practically important task for the service provider, and thus there have been many studies on this task.
no code implementations • ACL 2019 • Takuya Makino, Tomoya Iwakura, Hiroya Takamura, Manabu Okumura
The experimental results show that a state-of-the-art neural summarization model optimized with GOLC generates fewer overlength summaries while maintaining the fastest processing speed; only 6. 70{\%} overlength summaries on CNN/Daily and 7. 8{\%} on long summary of Mainichi, compared to the approximately 20{\%} to 50{\%} on CNN/Daily Mail and 10{\%} to 30{\%} on Mainichi with the other optimization methods.
no code implementations • WS 2019 • Yuta Hitomi, Yuya Taguchi, Hideaki Tamori, Ko Kikuta, Jiro Nishitoba, Naoaki Okazaki, Kentaro Inui, Manabu Okumura
However, because there is no corpus of headlines of multiple lengths for a given article, previous research on controlling output length in headline generation has not discussed whether the system outputs could be adequately evaluated without multiple references of different lengths.
no code implementations • WS 2018 • Abdurrisyad Fikri, Hiroya Takamura, Manabu Okumura
Recent neural models for response generation show good results in terms of general responses.
no code implementations • COLING 2018 • Arata Ugawa, Akihiro Tamura, Takashi Ninomiya, Hiroya Takamura, Manabu Okumura
To alleviate these problems, the encoder of the proposed model encodes the input word on the basis of its NE tag at each time step, which could reduce the ambiguity of the input word.
no code implementations • IJCNLP 2017 • Tatsuya Ishigaki, Hiroya Takamura, Manabu Okumura
In this research, we propose the task of question summarization.
Abstractive Text Summarization Community Question Answering +1
no code implementations • IJCNLP 2017 • Hidetaka Kamigaito, Katsuhiko Hayashi, Tsutomu Hirao, Hiroya Takamura, Manabu Okumura, Masaaki Nagata
The sequence-to-sequence (Seq2Seq) model has been successfully applied to machine translation (MT).
no code implementations • EMNLP 2017 • Tatsuya Aoki, Ryohei Sasano, Hiroya Takamura, Manabu Okumura
Our experimental results show that the model leveraging the context embedding outperforms other methods and provide us with findings, for example, on how to construct context embeddings and which corpus to use.
no code implementations • ACL 2017 • Shun Hasegawa, Yuta Kikuchi, Hiroya Takamura, Manabu Okumura
In English, high-quality sentence compression models by deleting words have been trained on automatically created large training datasets.
1 code implementation • EMNLP 2016 • Yuta Kikuchi, Graham Neubig, Ryohei Sasano, Hiroya Takamura, Manabu Okumura
Neural encoder-decoder models have shown great success in many sequence generation tasks.