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Found 8 papers, 2 papers with code
CMCL 2021 Shared Task on Eye-Tracking Prediction
The goal of the task is to predict 5 different token- level eye-tracking metrics of the Zurich Cognitive Language Processing Corpus (ZuCo).
The Viability of Best-worst Scaling and Categorical Data Label Annotation Tasks in Detecting Implicit Bias
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.
Incorporating Annotator Uncertainty into Representations of Discourse Relations
Annotation of discourse relations is a known difficult task, especially for non-expert annotators.
The distribution of discourse relations within and across turns in spontaneous conversation
Time pressure and topic negotiation may impose constraints on how people leverage discourse relations (DRs) in spontaneous conversational contexts.
Lost in Space Marking
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.
Will it Unblend?
Natural language processing systems often struggle with out-of-vocabulary (OOV) terms, which do not appear in training data.
NYTWIT: A Dataset of Novel Words in the New York Times
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.
