activity
20142023
most citedSimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation

38 citations · 51 across the 9 of their papers we have counts for

collaborators

7 papers

cs.CL20231 cited

A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training

Nitay Calderon, Subhabrata Mukherjee, Roi Reichart +1

Modern Natural Language Generation (NLG) models come with massive computational and storage requirements. In this work, we study the potential of compressing them, which is crucial…

cs.CL2022

Domain Adaptation from Scratch

Eyal Ben-David, Yftah Ziser, Roi Reichart

Natural language processing (NLP) algorithms are rapidly improving but often struggle when applied to out-of-distribution examples. A prominent approach to mitigate the domain gap…

cs.CL20221 cited

Multi-task Active Learning for Pre-trained Transformer-based Models

Guy Rotman, Roi Reichart

Multi-task learning, in which several tasks are jointly learned by a single model, allows NLP models to share information from multiple annotations and may facilitate better predic…

cs.CL201610 cited

Survey on the Use of Typological Information in Natural Language Processing

Helen O'Horan, Yevgeni Berzak, Ivan Vulić +2

In recent years linguistic typology, which classifies the world's languages according to their functional and structural properties, has been widely used to support multilingual NL…

cs.CL2016

Effective Combination of Language and Vision Through Model Composition and the R-CCA Method

Hagar Loeub, Roi Reichart

We address the problem of integrating textual and visual information in vector space models for word meaning representation. We first present the Residual CCA (R-CCA) method, that…

cs.CL2016

A Factorized Model for Transitive Verbs in Compositional Distributional Semantics

Lilach Edelstein, Roi Reichart

We present a factorized compositional distributional semantics model for the representation of transitive verb constructions. Our model first produces (subject, verb) and (verb, ob…