Bio
Hi, my name is Hila and I love thinking of and implementing algorithms that deal with natural language. I finished my masters in NLP 6 years ago and have been working at NICE ever since.
At NICE I build systems that discover insights from call center conversations, for example discovering prominent topics, analyzing agent’s conversational behavior and much more. I’m married to Yotam and a mother to Noga and Tamar 🙂
Bio
Hi, my name is Hila and I love thinking of and implementing algorithms that deal with natural language. I finished my masters in NLP 6 years ago and have been working at NICE ever since.
At NICE I build systems that discover insights from call center conversations, for example discovering prominent topics, analyzing agent’s conversational behavior and much more. I’m married to Yotam and a mother to Noga and Tamar 🙂
Abstract
In this work, I’ll show how we’re creating a system able to predict call center agent’s behavioral characteristics (e.g., knowledgeable, friendly etc.) using a Deep Learning based solution which also utilizes novel linguistic-based features.
These features try to model key aspects in the agent’s conversations, for example: the agent’s manner of speaking (e.g., fluent/dis-fluent), complexity of language (long sentences, unique words), level of adaptability (to change in customer’s sentiment) etc. We will show preliminary results using different settings and features and discuss future directions for research.
Abstract
In this work, I’ll show how we’re creating a system able to predict call center agent’s behavioral characteristics (e.g., knowledgeable, friendly etc.) using a Deep Learning based solution which also utilizes novel linguistic-based features.
These features try to model key aspects in the agent’s conversations, for example: the agent’s manner of speaking (e.g., fluent/dis-fluent), complexity of language (long sentences, unique words), level of adaptability (to change in customer’s sentiment) etc. We will show preliminary results using different settings and features and discuss future directions for research.
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 |
