48 citations · 57 across the 6 of their papers we have counts for
9 papers · 1 filter
Explaining Documents' Relevance to Search Queries
Razieh Rahimi, Youngwoo Kim, Hamed Zamani +1
We present GenEx, a generative model to explain search results to users beyond just showing matches between query and document words. Adding GenEx explanations to search results gr…
Cross-Market Product Recommendation
Hamed Bonab, Mohammad Aliannejadi, Ali Vardasbi +2
We study the problem of recommending relevant products to users in relatively resource-scarce markets by leveraging data from similar, richer in resource auxiliary markets. We hypo…
Query-driven Segment Selection for Ranking Long Documents
Youngwoo Kim, Razieh Rahimi, Hamed Bonab +1
Transformer-based rankers have shown state-of-the-art performance. However, their self-attention operation is mostly unable to process long sequences. One of the common approaches…
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…
Explaining Controversy on Social Media via Stance Summarization
Myungha Jang, James Allan
In an era in which new controversies rapidly emerge and evolve on social media, navigating social media platforms to learn about a new controversy can be an overwhelming task. In t…
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…