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 • 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 • 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 • 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 • 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 • 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
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
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.
no code implementations • 15 Nov 2023 • Yusuke Sakai, Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe
Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities in a KG.
1 code implementation • 17 Sep 2023 • Xincan Feng, Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe
Subsampling is effective in Knowledge Graph Embedding (KGE) for reducing overfitting caused by the sparsity in Knowledge Graph (KG) datasets.
1 code implementation • 3 Jun 2023 • Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe
This task consists of two parts: the first is to generate a table containing knowledge about an entity and its related image, and the second is to generate an image from an entity with a caption and a table containing related knowledge of the entity.
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)
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)
no code implementations • 13 Sep 2022 • Hidetaka Kamigaito, Katsuhiko Hayashi
In this article, we explain the recent advance of subsampling methods in knowledge graph embedding (KGE) starting from the original one used in word2vec.
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.
1 code implementation • 21 Jun 2022 • Hidetaka Kamigaito, Katsuhiko Hayashi
To solve this problem, we theoretically analyzed NS loss to assist hyperparameter tuning and understand the better use of the NS loss in KGE learning.
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.
no code implementations • 29 Sep 2021 • Hidetaka Kamigaito, Katsuhiko Hayashi
On the other hand, properties of the NS loss function that are considered important for learning, such as the relationship between the noise distribution and the number of negative samples, have not been investigated theoretically.
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.
1 code implementation • ACL 2021 • Hidetaka Kamigaito, Katsuhiko Hayashi
In knowledge graph embedding, the theoretical relationship between the softmax cross-entropy and negative sampling loss functions has not been investigated.
Ranked #14 on
Link Prediction
on FB15k-237
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)
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 • Chenlong Hu, Yukun Feng, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
This work presents multi-modal deep SVDD (mSVDD) for one-class text classification.
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 • 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, 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 • 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 • 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 • 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 • EMNLP 2018 • Tsutomu Hirao, Hidetaka Kamigaito, Masaaki Nagata
This paper tackles automation of the pyramid method, a reliable manual evaluation framework.
1 code implementation • ACL 2018 • Jun Suzuki, Sho Takase, Hidetaka Kamigaito, Makoto Morishita, Masaaki Nagata
This paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing.
Ranked #16 on
Constituency Parsing
on Penn Treebank
no code implementations • NAACL 2018 • Hidetaka Kamigaito, Katsuhiko Hayashi, Tsutomu Hirao, Masaaki Nagata
To solve this problem, we propose a higher-order syntactic attention network (HiSAN) that can handle higher-order dependency features as an attention distribution on LSTM hidden states.
Ranked #3 on
Sentence Compression
on Google Dataset
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).