Dota - Tianai Dong
Hi! I am a second-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.
My research interests lie at the intersection of machine learning, and cognitive neuroscience (with a focus on language). I am particularly interested in understanding the computational and cognitive principles that underlie human multimodal percept. I mainly use computational methods to study these questions, in conjunction with data and theories from neuroscience, linguistics, and psychology.
If you want to discuss any academia-related topics, please feel free to reach out to me :)
Email // Google Scholar // Twitter
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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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