Bio
Hila is a PhD student at Bar Ilan University in the field of Natural Language Processing and Deep Learning, under the supervision of Prof. Yoav Goldberg.
Prior to that, She obtained her M.Sc in Computer Science from the Hebrew University, under the supervision of Prof. Orna Kupferman. She is fascinated by languages and interested in relations between different languages and the way multilingual signals can be used for various tasks.
Bio
Hila is a PhD student at Bar Ilan University in the field of Natural Language Processing and Deep Learning, under the supervision of Prof. Yoav Goldberg.
Prior to that, She obtained her M.Sc in Computer Science from the Hebrew University, under the supervision of Prof. Orna Kupferman. She is fascinated by languages and interested in relations between different languages and the way multilingual signals can be used for various tasks.
Abstract
Word embeddings are widely used in the NLP community for a vast range of tasks. We will show that these models, which are derived from text corpora, reflect gender biases in society – a phenomenon that is pervasive and consistent across different word embedding models, causing serious concern.
Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. We will review these methods and inspect the resulting debiased embeddings.
Abstract
Word embeddings are widely used in the NLP community for a vast range of tasks. We will show that these models, which are derived from text corpora, reflect gender biases in society – a phenomenon that is pervasive and consistent across different word embedding models, causing serious concern.
Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. We will review these methods and inspect the resulting debiased embeddings.
Planned Agenda
| 8:45 | Reception and Breakfast |
|---|---|
| 9:30 | Opening words by Shir Meir Lador, Data Science team lead at Intuit |
| 9:45 | Key Note - Diane Chang - Making Chatbot Magic |
| 10:15 | Key Note - Chana Greene - Virtual Reality data to product Reality |
| 10:45 | Talk - Hagit Grushka-Cohen Simulating user activity for assessing the effect of sampling on DB activity monitoring anomaly Detection |
| 11:15 | Break |
| 11:30 | Talk - Inbar Naor - Lessons from building deep learning recommendation systems |
|---|---|
| 12:00 | Talk - Hila Gonen - Gender Bias in Word Embeddings |
| 12:30 | Talk - Yonit Hoffman - Data to the Rescue! Predicting & Preventing Accidents at Sea |
| 13:00 | Lunch |
| 14:00 | Roundtable Discussions and Posters |
| 15:00 | Roundtable & Conference closure |
| 15:15 | Tutorial - Chae Young-Lee - Dealing with the Lack of Audio Data |
Planned Agenda
| 8:45 | Reception and Breakfast |
|---|---|
| 9:30 | Opening words by Shir Meir Lador, Data Science team lead at Intuit |
| 9:45 | Key Note - Diane Chang - Making Chatbot Magic |
| 10:15 | Key Note - Chana Greene - Virtual Reality data to product Reality |
| 10:45 | Talk - Hagit Grushka-Cohen Simulating user activity for assessing the effect of sampling on DB activity monitoring anomaly Detection |
| 11:15 | Break |
| 11:30 | Talk - Inbar Naor - Lessons from building deep learning recommendation systems |
| 12:00 | Talk - Hila Gonen - Gender Bias in Word Embeddings |
| 12:30 | Talk - Yonit Hoffman - Data to the Rescue! Predicting & Preventing Accidents at Sea |
| 13:00 | Lunch |
| 14:00 | Roundtable Discussions and Posters |
| 15:00 | Roundtable & Conference closure |
| 15:15 | Tutorial - Chae Young-Lee - Dealing with the Lack of Audio Data |
