Dota - Tianai Dong
Hi! I am a third-year PhD student, affiliated with Multimodal Language Department at the Max Planck Institute for Psycholinguistics, and Predictive Brain Lab at the Donders Institute (Centre for Cognitive Neuroimaging). I am co-advised by Floris de Lange , Lea-Maria Schmitt
, Stefan Frank, and Paula Rubio-Fernández . I also work closely with Mariya Toneva at the Max Planck Institute for Software Systems. I am funded by an IMPRS fellowship.
I study how humans acquire, mentally represent, and generate predictions about language through our rich multimodal experiences. My approach combines computational methods with insights from neuroscience, linguistics, and psychology, with the dual goals of understanding the human mind and advancing artificial intelligence.
If you want to discuss any academia-related topics, please feel free to reach out to me :)
Email // Google Scholar // BlueSky
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I'm helping to organize this year's CCN in Amsterdam as part of the DEI Committee.
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Multimodal Video Transformers Partially Align with Multimodal Grounding and Compositionality in the Brain
Dota Tianai Dong,
Mariya Toneva
ICLR-MRL, 2023;
CCN, 2023;
Preprint, 2024
We propose to probe a pre-trained multimodal video transformer model, guided by insights from neuroscientific evidence on multimodal information processing in the human brain.
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Discogem: A crowdsourced corpus of genre-mixed implicit discourse relations
Merel Scholman
Dota Tianai Dong,
Frances Yung,
Vera Demberg
LREC, 2022;
We present DiscoGeM, a crowdsourced corpus of 6,505 implicit discourse relations from three genres: political speech,
literature, and encyclopedic text.
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Comparison of methods for explicit discourse connective identification across various domains
Merel Scholman
Dota Tianai Dong,
Frances Yung,
Vera Demberg
CODI, 2021;
We assess the performance on explicit
connective identification of four parse methods (PDTB e2e, Lin et al., 2014; the winner of CONLL2015, Wang and Lan, 2015; DisSent, Nie et al., 2019; and Discopy, Knaebel and Stede, 2020), along with a simple heuristic.
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Visually grounded follow-up questions: A dataset of spatial questions which require dialogue history
Dota Tianai Dong,
Alberto Testoni,
Luciana Benotti,
Raffaella Bernardi
Splurobonlp, 2021;
We define and evaluate a methodology for extracting history-dependent spatial questions from visual dialogues.
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