Search Results for author: Ali Ahmadvand

Found 13 papers, 3 papers with code

PREME: Preference-based Meeting Exploration through an Interactive Questionnaire

no code implementations5 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.

Supporting Complex Information-Seeking Tasks with Implicit Constraints

no code implementations2 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 interface lack an 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.

DeepCAT: Deep Category Representation for Query Understanding in E-commerce Search

no code implementations23 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.

CRAB: Class Representation Attentive BERT for Hate Speech Identification in Social Media

no code implementations25 Oct 2020 Sayyed M. Zahiri, Ali Ahmadvand

In recent years, social media platforms have hosted an explosion of hate speech and objectionable content.

Hate Speech Detection

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

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.

JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search

no code implementations28 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.

Active Learning

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

User Intent Inference for Web Search and Conversational Agents

no code implementations28 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.

General Classification 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

ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering

no code implementations7 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.

Answer Selection Multi-Task Learning

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