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- Tel Aviv UniversityIL42 papers
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- Ben-Gurion University of the NegevIL24 papers
- Hebrew University of JerusalemIL22 papers
- University of Maryland, College ParkUS20 papers
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- Centre National de la Recherche ScientifiqueFR17 papers
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7 papers · 2 filters
Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets
Ohad Rozen, Vered Shwartz, Roee Aharoni +1
Phenomenon-specific "adversarial" datasets have been recently designed to perform targeted stress-tests for particular inference types. Recent work (Liu et al., 2019a) proposed tha…
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution
Shany Barhom, Vered Shwartz, Alon Eirew +3
Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications. Despite the task's importance, research focus was given mostly to within-doc…
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation
Amit Moryossef, Yoav Goldberg, Ido Dagan
Data-to-text generation can be conceptually divided into two parts: ordering and structuring the information (planning), and generating fluent language describing the information (…
Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +1
In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set…
Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages
Shauli Ravfogel, Yoav Goldberg, Tal Linzen
How do typological properties such as word order and morphological case marking affect the ability of neural sequence models to acquire the syntax of a language? Cross-linguistic c…
ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction
Eva Vanmassenhove, Amit Moryossef, Alberto Poncelas +2
We present our system for the CLIN29 shared task on cross-genre gender detection for Dutch. We experimented with a multitude of neural models (CNN, RNN, LSTM, etc.), more "traditio…