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Found 10 papers, 4 papers with code
Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training
Translating to and from low-resource polysynthetic languages present numerous challenges for NMT.
Breeding Fillmore’s Chickens and Hatching the Eggs: Recombining Frames and Roles in Frame-Semantic Parsing
Frame-semantic parsers traditionally predict predicates, frames, and semantic roles in a fixed order.
Responsibility Perspective Transfer for Italian Femicide News
Different ways of linguistically expressing the same real-world event can lead to different perceptions of what happened.
Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports
We then train regression models that predict the salience of GBV participants with respect to different dimensions of perceived responsibility.
SOCIOFILLMORE: A Tool for Discovering Perspectives
SOCIOFILLMORE is a multilingual tool which helps to bring to the fore the focus or the perspective that a text expresses in depicting an event.
Kicktionary-LOME: A Domain-Specific Multilingual Frame Semantic Parsing Model for Football Language
This technical report introduces an adapted version of the LOME frame semantic parsing model (Xia et al., EACL 2021) which is capable of automatically annotating texts according to the "Kicktionary" domain-specific framenet resource.
Towards Reference-Aware FrameNet Annotation
In this paper, we introduce the task of using FrameNet to link structured information about real-world events to the conceptual frames used in texts describing these events.
Large-scale Cross-lingual Language Resources for Referencing and Framing
In this article, we lay out the basic ideas and principles of the project Framing Situations in the Dutch Language.
Toward Dialogue Modeling: A Semantic Annotation Scheme for Questions and Answers
The present study proposes an annotation scheme for classifying the content and discourse contribution of question-answer pairs.
From Brain Space to Distributional Space: The Perilous Journeys of fMRI Decoding
Despite returning promising results, our experiments also demonstrate that much work remains to be done before distributional representations can reliably be predicted from brain data.
