26 citations · 61 across the 14 of their papers we have counts for
15 papers
HIERA: Hierarchical Multi-Agent Relevance Assessment for Content Discovery Systems
Pritom Saha Akash, Phanideep Gampa, Chao Shen +2
Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs. While Large Languag…
REAPER: Reasoning based Retrieval Planning for Complex RAG Systems
Ashutosh Joshi, Sheikh Muhammad Sarwar, Samarth Varshney +3
Complex dialog systems often use retrieved evidence to facilitate factual responses. Such RAG (Retrieval Augmented Generation) systems retrieve from massive heterogeneous data stor…
Target Span Detection for Implicit Harmful Content
Nazanin Jafari, James Allan, Sheikh Muhammad Sarwar
Identifying the targets of hate speech is a crucial step in grasping the nature of such speech and, ultimately, in improving the detection of offensive posts on online forums. Much…
Scalable and Effective Generative Information Retrieval
Hansi Zeng, Chen Luo, Bowen Jin +3
Recent research has shown that transformer networks can be used as differentiable search indexes by representing each document as a sequences of document ID tokens. These generativ…
Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval
Zhiqi Huang, Hamed Bonab, Sheikh Muhammad Sarwar +2
Pretrained contextualized representations offer great success for many downstream tasks, including document ranking. The multilingual versions of such pretrained representations pr…
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction
Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar +6
Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervisi…
Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach
Sheikh Muhammad Sarwar, Vanessa Murdock
Online harassment in the form of hate speech has been on the rise in recent years. Addressing the issue requires a combination of content moderation by people, aided by automatic d…
Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence
Andrew Halterman, Katherine A. Keith, Sheikh Muhammad Sarwar +1
Automated event extraction in social science applications often requires corpus-level evaluations: for example, aggregating text predictions across metadata and unbiased estimates…
A Neighbourhood Framework for Resource-Lean Content Flagging
Sheikh Muhammad Sarwar, Dimitrina Zlatkova, Momchil Hardalov +3
We propose a novel framework for cross-lingual content flagging with limited target-language data, which significantly outperforms prior work in terms of predictive performance. Th…
Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go
Arnav Arora, Preslav Nakov, Momchil Hardalov +8
The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and ha…
Semantic Driven Fielded Entity Retrieval
Shahrzad Naseri, Sheikh Muhammad Sarwar, James Allan
A common approach for knowledge-base entity search is to consider an entity as a document with multiple fields. Models that focus on matching query terms in different fields are po…
A Multi-Task Architecture on Relevance-based Neural Query Translation
Sheikh Muhammad Sarwar, Hamed Bonab, James Allan
We describe a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query translation. The translati…
Named Entity Recognition with Extremely Limited Data
John Foley, Sheikh Muhammad Sarwar, James Allan
Traditional information retrieval treats named entity recognition as a pre-indexing corpus annotation task, allowing entity tags to be indexed and used during search. Named entity…
Term Relevance Feedback for Contextual Named Entity Retrieval
Sheikh Muhammad Sarwar, John Foley, James Allan
We address the role of a user in Contextual Named Entity Retrieval (CNER), showing (1) that user identification of important context-bearing terms is superior to automated approach…
Two-stage Cascaded Classifier for Purchase Prediction
Sheikh Muhammad Sarwar, Mahamudul Hasan, Dmitry I. Ignatov
In this paper we describe our machine learning solution for the RecSys Challenge, 2015. We have proposed a time efficient two-stage cascaded classifier for the prediction of buy se…