160 citations · 253 across the 23 of their papers we have counts for
10 papers · 1 filter
Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information
Shadi Iskander, Kira Radinsky, Yonatan Belinkov
Mitigating social biases typically requires identifying the social groups associated with each data sample. In this paper, we present DAFair, a novel approach to address social bia…
Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient-Based Projection
Shadi Iskander, Kira Radinsky, Yonatan Belinkov
Natural language processing models tend to learn and encode social biases present in the data. One popular approach for addressing such biases is to eliminate encoded information f…
What If: Generating Code to Answer Simulation Questions
Gal Peretz, Kira Radinsky
Many texts, especially in chemistry and biology, describe complex processes. We focus on texts that describe a chemical reaction process and questions that ask about the process's…
Temporal Attention for Language Models
Guy D. Rosin, Kira Radinsky
Pretrained language models based on the transformer architecture have shown great success in NLP. Textual training data often comes from the web and is thus tagged with time-specif…
Time Masking for Temporal Language Models
Guy D. Rosin, Ido Guy, Kira Radinsky
Our world is constantly evolving, and so is the content on the web. Consequently, our languages, often said to mirror the world, are dynamic in nature. However, most current contex…
Generating Timelines by Modeling Semantic Change
Guy D. Rosin, Kira Radinsky
Though languages can evolve slowly, they can also react strongly to dramatic world events. By studying the connection between words and events, it is possible to identify which eve…
Learning to Focus when Ranking Answers
Dana Sagi, Tzoof Avny, Kira Radinsky +1
One of the main challenges in ranking is embedding the query and document pairs into a joint feature space, which can then be fed to a learning-to-rank algorithm. To achieve this r…
Learning Word Relatedness over Time
Guy D. Rosin, Eytan Adar, Kira Radinsky
Search systems are often focused on providing relevant results for the "now", assuming both corpora and user needs that focus on the present. However, many corpora today reflect si…
Named Entity Disambiguation for Noisy Text
Yotam Eshel, Noam Cohen, Kira Radinsky +3
We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly n…
Learning to Predict from Textual Data
Kira Radinsky, Sagie Davidovich, Shaul Markovitch
Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such…