no code implementations • 5 Apr 2023 • ZiHao Wang, Ali Ahmadvand, Jason Choi, Payam Karisani, Eugene Agichtein
Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs.
1 code implementation • 5 May 2022 • Negar Arabzadeh, Ali Ahmadvand, Julia Kiseleva, Yang Liu, Ahmed Hassan Awadallah, Ming Zhong, Milad Shokouhi
The recent increase in the volume of online meetings necessitates automated tools for managing and organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it.
no code implementations • 2 May 2022 • Ali Ahmadvand, Negar Arabzadeh, Julia Kiseleva, Patricio Figueroa Sanz, Xin Deng, Sujay Jauhar, Michael Gamon, Eugene Agichtein, Ned Friend, Aniruddha
Current interactive systems with natural language interfaces lack the ability to understand a complex information-seeking request which expresses several implicit constraints at once, and there is no prior information about user preferences e. g.,"find hiking trails around San Francisco which are accessible with toddlers and have beautiful scenery in summer", where output is a list of possible suggestions for users to start their exploration.
no code implementations • 23 Apr 2021 • Ali Ahmadvand, Surya Kallumadi, Faizan Javed, Eugene Agichtein
Mapping a search query to a set of relevant categories in the product taxonomy is a significant challenge in e-commerce search for two reasons: 1) Training data exhibits severe class imbalance problem due to biased click behavior, and 2) queries with little customer feedback (e. g., tail queries) are not well-represented in the training set, and cause difficulties for query understanding.
no code implementations • 23 Apr 2021 • Ali Ahmadvand, Sayyed M. Zahiri, Simon Hughes, Khalifa Al Jadda, Surya Kallumadi, Eugene Agichtein
Query categorization is an essential part of query intent understanding in e-commerce search.
no code implementations • 25 Oct 2020 • Sayyed M. Zahiri, Ali Ahmadvand
In recent years, social media platforms have hosted an explosion of hate speech and objectionable content.
no code implementations • 10 Sep 2020 • Sarah E. Finch, James D. Finch, Ali Ahmadvand, Ingyu, Choi, Xiangjue Dong, Ruixiang Qi, Harshita Sahijwani, Sergey Volokhin, Zihan Wang, ZiHao Wang, Jinho D. Choi
Inspired by studies on the overwhelming presence of experience-sharing in human-human conversations, Emora, the social chatbot developed by Emory University, aims to bring such experience-focused interaction to the current field of conversational AI.
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 • 28 May 2020 • Ali Ahmadvand
To address these research challenges, my thesis work focuses on: 1) Utterance topic and intent classification for conversational agents 2) Query intent mining and classification for Web search engines, focusing on the e-commerce domain.
no code implementations • 28 May 2020 • Ali Ahmadvand, Surya Kallumadi, Faizan Javed, Eugene Agichtein
In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query.
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.
no code implementations • 28 May 2020 • Ali Ahmadvand, Harshita Sahijwani, Eugene Agichtein
A topic suggested by the agent should be relevant to the person, appropriate for the conversation context, and the agent should have something interesting to say about it.
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.
no code implementations • 7 Aug 2017 • Ali Ahmadvand, Jinho D. Choi
In addition, using ISS-MULT could finely improve the MULT method for question answering tasks, and these improvements prove more significant in the answer triggering task.