Abstract is missing.
- LOWRECORP: the Low-Resource NLG Corpus Building ChallengeKhyathi Raghavi Chandu, David M. Howcroft, Dimitra Gkatzia, Yi-Ling Chung, Yufang Hou 0001, Chris Chinenye Emezue, Pawan Rajpoot, Tosin P. Adewumi. 1-9 [doi]
- Long Story Generation ChallengeNikolay Mikhaylovskiy. 10-16 [doi]
- Visually Grounded Story Generation ChallengeXudong Hong 0002, Khushboo Mehra, Asad B. Sayeed, Vera Demberg. 17-22 [doi]
- The VDG Challenge: Response Generation and Evaluation in Collaborative Visual DialogueNikolai Ilinykh, Simon Dobnik. 23-30 [doi]
- Identifying Feedback Types to Augment Feedback Comment GenerationMaja Stahl, Henning Wachsmuth. 31-36 [doi]
- Error syntax aware augmentation of feedback comment generation datasetNikolay Babakov, Maria Lysyuk, Alexander Shvets, Lilya Kazakova, Alexander Panchenko. 37-44 [doi]
- A Report on FCG GenChal 2022: Shared Task on Feedback Comment Generation for Language LearnersRyo Nagata, Masato Hagiwara, Kazuaki Hanawa, Masato Mita. 45-52 [doi]
- Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help?Shabnam Behzad, Amir Zeldes, Nathan Schneider 0001. 53-59 [doi]
- Retrieval, Masking, and Generation: Feedback Comment Generation using Masked Comment ExamplesMana Ihori, Hiroshi Sato, Tomohiro Tanaka, Ryo Masumura. 60-67 [doi]
- TMU Feedback Comment Generation System Using Pretrained Sequence-to-Sequence Language ModelsNaoya Ueda, Mamoru Komachi. 68-73 [doi]
- The Tokyo Tech and AIST System at the GenChal 2022 Shared Task on Feedback Comment GenerationShota Koyama, Hiroya Takamura, Naoaki Okazaki. 74-78 [doi]
- Feedback comment generation using predicted grammatical termsKunitaka Jimichi, Kotaro Funakoshi, Manabu Okumura. 79-83 [doi]
- AIWolfDial 2023: Summary of Natural Language Division of 5th International AIWolf ContestYoshinobu Kano, Neo Watanabe, Kaito Kagaminuma, Claus Aranha, Jaewon Lee, Benedek Hauer, Hisaichi Shibata, Soichiro Miki, Yuta Nakamura, Takuya Okubo, Soga Shigemura, Rei Ito, Kazuki Takashima, Tomoki Fukuda, Masahiro Wakutani, Tomoya Hatanaka, Mami Uchida, Mikio Abe, Akihiro Mikami, Takashi Otsuki, Zhiyang Qi, Kei Harada, Michimasa Inaba, Daisuke Katagami, Hirotaka Osawa, Fujio Toriumi. 84-100 [doi]
- Team Zoom @ AutoMin 2023: Utilizing Topic Segmentation And LLM Data Augmentation For Long-Form Meeting SummarizationFelix Schneider, Marco Turchi. 101-107 [doi]
- Team Synapse @ AutoMin 2023: Leveraging BART-Based Models for Automatic Meeting MinutingKristýna Klesnilová, Michelle Elizabeth. 108-113 [doi]
- Team Iterate @ AutoMin 2023 - Experiments with Iterative MinutingFrantisek Kmjec, Ondrej Bojar. 114-120 [doi]
- Darbarer @ AutoMin2023: Transcription simplification for concise minute generation from multi-party conversationsIsmaël Rousseau, Loïc Fosse, Youness Dkhissi, Géraldine Damnati, Gwénolé Lecorvé. 121-131 [doi]
- Team NTR @ AutoMin 2023: Dolly LLM Improves Minuting Performance, Semantic Segmentation Doesn'tEugene Borisov, Nikolay Mikhaylovskiy. 132-137 [doi]
- Overview of the Second Shared Task on Automatic Minuting (AutoMin) at INLG 2023Tirthankar Ghosal, Ondrej Bojar, Marie Hledíková, Tom Kocmi, Anna Nedoluzhko. 138-167 [doi]