A Benchmark Dataset for Automatic Detection of Claims and Evidence in the Context of Controversial Topics

no code implementations • WS 2014 • Ehud Aharoni, Anatoly Polnarov, Tamar Lavee, Daniel Hershcovich, Ran Levy, Ruty Rinott, Dan Gutfreund, Noam Slonim

Claims on demand -- an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora

no code implementations • COLING 2014 • Noam Slonim, Ehud Aharoni, Carlos Alzate, Roy Bar-Haim, Yonatan Bilu, Lena Dankin, Iris Eiron, Daniel Hershcovich, Shay Hummel, Mitesh Khapra, Tamar Lavee, Ran Levy, Paul Matchen, Anatoly Polnarov, Vikas Raykar, Ruty Rinott, Amrita Saha, Naama Zwerdling, David Konopnicki, Dan Gutfreund

Decision Making

TR9856: A Multi-word Term Relatedness Benchmark

no code implementations • IJCNLP 2015 • Ran Levy, Liat Ein-Dor, Shay Hummel, Ruty Rinott, Noam Slonim

Show Me Your Evidence - an Automatic Method for Context Dependent Evidence Detection

no code implementations • EMNLP 2015 • Ruty Rinott, Lena Dankin, Carlos Alzate Perez, Mitesh M. Khapra, Ehud Aharoni, Noam Slonim

Semantic Relatedness of Wikipedia Concepts -- Benchmark Data and a Working Solution

no code implementations • LREC 2018 • Liat Ein Dor, Alon Halfon, Yoav Kantor, Ran Levy, Yosi Mass, Ruty Rinott, Eyal Shnarch, Noam Slonim

Entity Linking Learning-To-Rank

XNLI: Evaluating Cross-lingual Sentence Representations

10 code implementations • EMNLP 2018 • Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk, Veselin Stoyanov

State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models.

Cross-Lingual Natural Language Inference Machine Translation +2

MLQA: Evaluating Cross-lingual Extractive Question Answering

4 code implementations • ACL 2020 • Patrick Lewis, Barlas Oğuz, Ruty Rinott, Sebastian Riedel, Holger Schwenk

An alternative to building large monolingual training datasets is to develop cross-lingual systems which can transfer to a target language without requiring training data in that language.

Extractive Question-Answering Machine Translation +1

Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefixes

no code implementations • 22 May 2023 • Kuan-Hao Huang, Liang Tan, Rui Hou, Sinong Wang, Amjad Almahairi, Ruty Rinott

Fine-tuning a large pre-trained language model for each downstream task causes computational burdens in the inference time due to several times of forward passes.

Language Modelling