35 citations · 54 across the 6 of their papers we have counts for
6 papers · 1 filter
Relational Graph Convolutional Neural Networks for Multihop Reasoning: A Comparative Study
Ieva Staliūnaitė, Philip John Gorinski, Ignacio Iacobacci
Multihop Question Answering is a complex Natural Language Processing task that requires multiple steps of reasoning to find the correct answer to a given question. Previous researc…
Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation
Ieva Staliūnaitė, Philip John Gorinski, Ignacio Iacobacci
Determining the plausibility of causal relations between clauses is a commonsense reasoning task that requires complex inference ability. The general approach to this task is to tr…
Improving End-to-End Speech-to-Intent Classification with Reptile
Yusheng Tian, Philip John Gorinski
End-to-end spoken language understanding (SLU) systems have many advantages over conventional pipeline systems, but collecting in-domain speech data to train an end-to-end system i…
Show Us the Way: Learning to Manage Dialog from Demonstrations
Gabriel Gordon-Hall, Philip John Gorinski, Gerasimos Lampouras +1
We present our submission to the End-to-End Multi-Domain Dialog Challenge Track of the Eighth Dialog System Technology Challenge. Our proposed dialog system adopts a pipeline archi…
Learning Dialog Policies from Weak Demonstrations
Gabriel Gordon-Hall, Philip John Gorinski, Shay B. Cohen
Deep reinforcement learning is a promising approach to training a dialog manager, but current methods struggle with the large state and action spaces of multi-domain dialog systems…
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches
Philip John Gorinski, Honghan Wu, Claire Grover +6
This work investigates multiple approaches to Named Entity Recognition (NER) for text in Electronic Health Record (EHR) data. In particular, we look into the application of (i) rul…