most citedProactive Prioritization of App Issues via Contrastive Learning

8 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Training Zero-Shot Generalizable End-to-End Task-Oriented Dialog System Without Turn-level Dialog Annotations

Adib Mosharrof, A. B. Siddique

Task-oriented dialogue (TOD) systems enable users to achieve their goals through natural language interactions. Traditionally, these systems have relied on turn-level manually anno…

cs.CL2023

Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema

Adib Mosharrof, M. H. Maqbool, A. B. Siddique

Task-oriented dialog systems empower users to accomplish their goals by facilitating intuitive and expressive natural language interactions. State-of-the-art approaches in task-ori…

cs.CL2023★ 1 cited

Toward Open-domain Slot Filling via Self-supervised Co-training

Adib Mosharrof, Moghis Fereidouni, A. B. Siddique

Slot filling is one of the critical tasks in modern conversational systems. The majority of existing literature employs supervised learning methods, which require labeled training…

cs.IR2023

MobileRec: A Large-Scale Dataset for Mobile Apps Recommendation

M. H. Maqbool, Umar Farooq, Adib Mosharrof +2

Recommender systems have become ubiquitous in our digital lives, from recommending products on e-commerce websites to suggesting movies and music on streaming platforms. Existing r…

cs.IR2023★ 8 cited

Proactive Prioritization of App Issues via Contrastive Learning

Moghis Fereidouni, Adib Mosharrof, Umar Farooq +1

Mobile app stores produce a tremendous amount of data in the form of user reviews, which is a huge source of user requirements and sentiments; such reviews allow app developers to…