no code implementations • 8 Feb 2024 • Ivana Balažević, Yuge Shi, Pinelopi Papalampidi, Rahma Chaabouni, Skanda Koppula, Olivier J. Hénaff
Most transformer-based video encoders are limited to short temporal contexts due to their quadratic complexity.
no code implementations • 12 Dec 2023 • Pinelopi Papalampidi, Skanda Koppula, Shreya Pathak, Justin Chiu, Joe Heyward, Viorica Patraucean, Jiajun Shen, Antoine Miech, Andrew Zisserman, Aida Nematzdeh
Understanding long, real-world videos requires modeling of long-range visual dependencies.
no code implementations • 10 Oct 2022 • Pinelopi Papalampidi, Mirella Lapata
In this paper, we focus on video-to-text summarization and investigate how to best utilize multimodal information for summarizing long inputs (e. g., an hour-long TV show) into long outputs (e. g., a multi-sentence summary).
no code implementations • 3 Feb 2022 • Pinelopi Papalampidi, Kris Cao, Tomas Kocisky
Large pre-trained language models (LMs) have demonstrated impressive capabilities in generating long, fluent text; however, there is little to no analysis on their ability to maintain entity coherence and consistency.
no code implementations • 16 Nov 2021 • Pinelopi Papalampidi, Frank Keller, Mirella Lapata
Movie trailers perform multiple functions: they introduce viewers to the story, convey the mood and artistic style of the film, and encourage audiences to see the movie.
1 code implementation • 14 Dec 2020 • Pinelopi Papalampidi, Frank Keller, Mirella Lapata
We summarize full-length movies by creating shorter videos containing their most informative scenes.
2 code implementations • ACL 2020 • Pinelopi Papalampidi, Frank Keller, Lea Frermann, Mirella Lapata
Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront.
no code implementations • IJCNLP 2019 • Pinelopi Papalampidi, Frank Keller, Mirella Lapata
According to screenwriting theory, turning points (e. g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and segment the screenplay into thematic units (e. g., setup, complications, aftermath).
3 code implementations • SEMEVAL 2018 • Christos Baziotis, Nikos Athanasiou, Pinelopi Papalampidi, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, Alexandros Potamianos
In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets".
no code implementations • SEMEVAL 2017 • Athanasia Kolovou, Filippos Kokkinos, Aris Fergadis, Pinelopi Papalampidi, Elias Iosif, Mal, Nikolaos rakis, Elisavet Palogiannidi, Haris Papageorgiou, Shrikanth Narayanan, Alex Potamianos, ros
In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter.