no code implementations • ACL 2020 • Matan Orbach, Yonatan Bilu, Assaf Toledo, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
An educated and informed consumption of media content has become a challenge in modern times.
no code implementations • WS 2019 • Tamar Lavee, Lili Kotlerman, Matan Orbach, Yonatan Bilu, Michal Jacovi, Ranit Aharonov, Noam Slonim
Recent advancements in machine reading and listening comprehension involve the annotation of long texts.
no code implementations • IJCNLP 2019 • Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results.
no code implementations • 3 Sep 2019 • Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results.
no code implementations • IJCNLP 2019 • Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog.
no code implementations • WS 2019 • Tamar Lavee, Matan Orbach, Lili Kotlerman, Yoav Kantor, Shai Gretz, Lena Dankin, Shachar Mirkin, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
To this end, we collected a large dataset of $400$ speeches in English discussing $200$ controversial topics, mined claims for each topic, and asked annotators to identify the mined claims mentioned in each speech.
no code implementations • EMNLP 2018 • Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset.
Automatic Speech Recognition (ASR) Machine Reading Comprehension +1
no code implementations • LREC 2018 • Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim
This paper describes an English audio and textual dataset of debating speeches, a unique resource for the growing research field of computational argumentation and debating technologies.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1