12 citations · 49 across the 21 of their papers we have counts for
9 papers · 1 filter
Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?
Neeraj Varshney, Man Luo, Chitta Baral
Recent state-of-the-art open-domain QA models are typically based on a two stage retriever-reader approach in which the retriever first finds the relevant knowledge/passages and th…
A Study on the Efficiency and Generalization of Light Hybrid Retrievers
Man Luo, Shashank Jain, Anchit Gupta +6
Hybrid retrievers can take advantage of both sparse and dense retrievers. Previous hybrid retrievers leverage indexing-heavy dense retrievers. In this work, we study "Is it possibl…
BioTABQA: Instruction Learning for Biomedical Table Question Answering
Man Luo, Sharad Saxena, Swaroop Mishra +2
Table Question Answering (TQA) is an important but under-explored task. Most of the existing QA datasets are in unstructured text format and only few of them use tables as the cont…
Neural Retriever and Go Beyond: A Thesis Proposal
Man Luo
Information Retriever (IR) aims to find the relevant documents (e.g. snippets, passages, and articles) to a given query at large scale. IR plays an important role in many tasks suc…
In-BoXBART: Get Instructions into Biomedical Multi-Task Learning
Mihir Parmar, Swaroop Mishra, Mirali Purohit +3
Single-task models have proven pivotal in solving specific tasks; however, they have limitations in real-world applications where multi-tasking is necessary and domain shifts are e…
Improving Contrastive Learning with Model Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity a…
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness
Tejas Gokhale, Swaroop Mishra, Man Luo +2
Data modification, either via additional training datasets, data augmentation, debiasing, and dataset filtering, has been proposed as an effective solution for generalizing to out-…
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering
Man Luo, Kazuma Hashimoto, Semih Yavuz +3
While both extractive and generative readers have been successfully applied to the Question Answering (QA) task, little attention has been paid toward the systematic comparison of…
Improving Biomedical Information Retrieval with Neural Retrievers
Man Luo, Arindam Mitra, Tejas Gokhale +1
Information retrieval (IR) is essential in search engines and dialogue systems as well as natural language processing tasks such as open-domain question answering. IR serve an impo…