activity
20182020
most citedSaliency Learning: Teaching the Model Where to Pay Attention

12 citations · 15 across the 2 of their papers we have counts for

collaborators

9 papers

cs.IR20203 cited

Relation Extraction with Explanation

Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini +1

Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences. Effort…

cs.CL2019

Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation

Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini +2

We present a new local entity disambiguation system. The key to our system is a novel approach for learning entity representations. In our approach we learn an entity aware extensi…

cs.CL201912 cited

Saliency Learning: Teaching the Model Where to Pay Attention

Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi +1

Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances. However, due to their opacity, such models are hard to interpret and trust. Recen…

cs.CL2018

Attentional Multi-Reading Sarcasm Detection

Reza Ghaeini, Xiaoli Z. Fern, Prasad Tadepalli

Recognizing sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and real world facts. Most of the c…

cs.CL2018

Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference

Reza Ghaeini, Xiaoli Z. Fern, Prasad Tadepalli

Deep learning models have achieved remarkable success in natural language inference (NLI) tasks. While these models are widely explored, they are hard to interpret and it is often…

cs.CL2018

Joint Neural Entity Disambiguation with Output Space Search

Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini +3

In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS). Giv…