24 citations · 118 across the 25 of their papers we have counts for
5 papers · 1 filter
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering
Kaixin Ma, Jonathan Francis, Quanyang Lu +2
Non-extractive commonsense QA remains a challenging AI task, as it requires systems to reason about, synthesize, and gather disparate pieces of information, in order to generate re…
Pruning Algorithms for Low-Dimensional Non-metric k-NN Search: A Case Study
Leonid Boytsov, Eric Nyberg
We focus on low-dimensional non-metric search, where tree-based approaches permit efficient and accurate retrieval while having short indexing time. These methods rely on space par…
Accurate and Fast Retrieval for Complex Non-metric Data via Neighborhood Graphs
Leonid Boytsov, Eric Nyberg
We demonstrate that a graph-based search algorithm-relying on the construction of an approximate neighborhood graph-can directly work with challenging non-metric and/or non-symmetr…
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations
Vinayshekhar Bannihatti Kumar, Ashwin Srinivasan, Aditi Chaudhary +3
This paper presents the submissions by Team Dr.Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain. Our system is based on th…
Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment
Hemant Pugaliya, Karan Saxena, Shefali Garg +4
Parallel deep learning architectures like fine-tuned BERT and MT-DNN, have quickly become the state of the art, bypassing previous deep and shallow learning methods by a large marg…