Search Results for author: Jason Ingyu Choi

Found 6 papers, 3 papers with code

Semantic Product Search for Matching Structured Product Catalogs in E-Commerce

no code implementations18 Aug 2020 Jason Ingyu Choi, Surya Kallumadi, Bhaskar Mitra, Eugene Agichtein, Faizan Javed

Retrieving all semantically relevant products from the product catalog is an important problem in E-commerce.

Would You Like to Hear the News? Investigating Voice-BasedSuggestions for Conversational News Recommendation

no code implementations2 Jun 2020 Harshita Sahijwani, Jason Ingyu Choi, Eugene Agichtein

One of the key benefits of voice-based personal assistants is the potential to proactively recommend relevant and interesting information.

News Recommendation

Quantifying the Effects of Prosody Modulation on User Engagement and Satisfaction in Conversational Systems

no code implementations2 Jun 2020 Jason Ingyu Choi, Eugene Agichtein

To accomplish this, we report results obtained from a large-scale empirical study that measures the effects of prosodic modulation on user behavior and engagement across multiple conversation domains, both immediately after each turn, and at the overall conversation level.

Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems

1 code implementation2 Jun 2020 Jason Ingyu Choi, Ali Ahmadvand, Eugene Agichtein

The insights from our study can enable more intelligent conversational systems, which could adapt in real-time to the inferred user satisfaction and engagement.

ConCET: Entity-Aware Topic Classification for Open-Domain Conversational Agents

1 code implementation28 May 2020 Ali Ahmadvand, Harshita Sahijwani, Jason Ingyu Choi, Eugene Agichtein

Our results show that ConCET significantly improves topic classification performance on both datasets, including 8-10% improvements over state-of-the-art deep learning methods.

Classification General Classification +1

Contextual Dialogue Act Classification for Open-Domain Conversational Agents

1 code implementation28 May 2020 Ali Ahmadvand, Jason Ingyu Choi, Eugene Agichtein

Furthermore, our results show that fine-tuning the CDAC model on a small sample of manually labeled human-machine conversations allows CDAC to more accurately predict dialogue acts in real users' conversations, suggesting a promising direction for future improvements.

Classification Dialogue Act Classification +3

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