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.
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 • 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 • 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 • 12 Oct 2022 • Kotaro Funakoshi
This paper presents Non-Axiomatic Term Logic (NATL) as a theoretical computational framework of humanlike symbolic reasoning in artificial intelligence.
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 • 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 • 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 • WS 2018 • Ryo Nagata, Tomoya Mizumoto, Yuta Kikuchi, Yoshifumi Kawasaki, Kotaro Funakoshi
Based on the discussion of possible causes of POS tagging errors in learner English, we show that deep neural models are particularly suitable for this.
no code implementations • LREC 2016 • Ryuichiro Higashinaka, Kotaro Funakoshi, Yuka Kobayashi, Michimasa Inaba
Dialogue breakdown detection is a promising technique in dialogue systems.