Search Results for author: Harshita Sahijwani

Found 6 papers, 1 papers with code

Contextual Response Interpretation for Automated Structured Interviews: A Case Study in Market Research

no code implementations30 Apr 2023 Harshita Sahijwani, Kaustubh Dhole, Ankur Purwar, Venugopal Vasudevan, Eugene Agichtein

Structured interviews are used in many settings, importantly in market research on topics such as brand perception, customer habits, or preferences, which are critical to product development, marketing, and e-commerce at large.

Marketing Multiple-choice +1

Emora: An Inquisitive Social Chatbot Who Cares For You

no code implementations10 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.

Chatbot intent-classification +1

Would you Like to Talk about Sports Now? Towards Contextual Topic Suggestion for Open-Domain Conversational Agents

no code implementations28 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.

Collaborative Filtering

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

SimDoc: Topic Sequence Alignment based Document Similarity Framework

no code implementations15 Nov 2016 Gaurav Maheshwari, Priyansh Trivedi, Harshita Sahijwani, Kunal Jha, Sourish Dasgupta, Jens Lehmann

Document similarity is the problem of estimating the degree to which a given pair of documents has similar semantic content.

Clustering Question Answering +2

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