no code implementations • COLING 2022 • Jason Ingyu Choi, Saar Kuzi, Nikhita Vedula, Jie Zhao, Giuseppe Castellucci, Marcus Collins, Shervin Malmasi, Oleg Rokhlenko, Eugene Agichtein
Conversational Task Assistants (CTAs) are conversational agents whose goal is to help humans perform real-world tasks.
no code implementations • 21 Nov 2023 • Simone Filice, Jason Ingyu Choi, Giuseppe Castellucci, Eugene Agichtein, Oleg Rokhlenko
Experiments on three tasks, i. e., Shopping Utterance Generation, Product Question Generation and Query Auto Completion, demonstrate that our metrics are effective for evaluating STG tasks, and improve the agreement with human judgement up to 20% with respect to common NLG metrics.
no code implementations • 18 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.
no code implementations • 2 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.
1 code implementation • 2 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.
no code implementations • 2 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.
1 code implementation • 28 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.
1 code implementation • 28 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.