15 citations · 24 across the 6 of their papers we have counts for
6 papers
Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank
Debjyoti Saha Roy, Byron C. Wallace, Javed A. Aslam
State-of-the-art Extreme Multi-Label Text Classification models rely on multi-label attention to focus on key tokens in input text, but learning good attention weights is challengi…
A Complex KBQA System using Multiple Reasoning Paths
Kechen Qin, Yu Wang, Cheng Li +4
Multi-hop knowledge based question answering (KBQA) is a complex task for natural language understanding. Many KBQA approaches have been proposed in recent years, and most of them…
Adapting RNN Sequence Prediction Model to Multi-label Set Prediction
Kechen Qin, Cheng Li, Virgil Pavlu +1
We present an adaptation of RNN sequence models to the problem of multi-label classification for text, where the target is a set of labels, not a sequence. Previous such RNN models…
Revealing the Basis: Ordinal Embedding Through Geometry
Jesse Anderton, Virgil Pavlu, Javed Aslam
Ordinal Embedding places n objects into R^d based on comparisons such as "a is closer to b than c." Current optimization-based approaches suffer from scalability problems and an ab…
Measuring Human-perceived Similarity in Heterogeneous Collections
Jesse Anderton, Pavel Metrikov, Virgil Pavlu +1
We present a technique for estimating the similarity between objects such as movies or foods whose proper representation depends on human perception. Our technique combines a modes…
Regularizing Model Complexity and Label Structure for Multi-Label Text Classification
Bingyu Wang, Cheng Li, Virgil Pavlu +1
Multi-label text classification is a popular machine learning task where each document is assigned with multiple relevant labels. This task is challenging due to high dimensional f…