no code implementations • NAACL (CMCL) 2021 • Nora Hollenstein, Emmanuele Chersoni, Cassandra L. Jacobs, Yohei Oseki, Laurent Prévot, Enrico Santus
The goal of the task is to predict 5 different token- level eye-tracking metrics of the Zurich Cognitive Language Processing Corpus (ZuCo).
no code implementations • NLPerspectives (LREC) 2022 • Parker Glenn, Cassandra L. Jacobs, Marvin Thielk, Yi Chu
We identify several shortcomings of BWS relative to traditional categorical annotation: (1) When compared to categorical annotation, we estimate BWS takes approximately 4. 5x longer to complete; (2) BWS does not scale well to large annotation tasks with sparse target phenomena; (3) The high correlation between BWS and the traditional task shows that the benefits of BWS can be recovered from a simple categorically annotated, non-aggregated dataset.
no code implementations • 14 Aug 2023 • S. Magalí López Cortez, Cassandra L. Jacobs
Annotation of discourse relations is a known difficult task, especially for non-expert annotators.
no code implementations • 7 Jul 2023 • S. Magalí López Cortez, Cassandra L. Jacobs
Time pressure and topic negotiation may impose constraints on how people leverage discourse relations (DRs) in spontaneous conversational contexts.
no code implementations • 2 Aug 2022 • Cassandra L. Jacobs, Yuval Pinter
We look at a decision taken early in training a subword tokenizer, namely whether it should be the word-initial token that carries a special mark, or the word-final one.
1 code implementation • SCiL 2021 • Yuval Pinter, Cassandra L. Jacobs, Jacob Eisenstein
Natural language processing systems often struggle with out-of-vocabulary (OOV) terms, which do not appear in training data.
1 code implementation • COLING 2020 • Yuval Pinter, Cassandra L. Jacobs, Max Bittker
We present baseline results for both uncontextual and contextual prediction of novelty class, showing that there is room for improvement even for state-of-the-art NLP systems.