This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge.
In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets".
In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets".
In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction".
We investigate entity linking in the context of a question answering task and present a jointly optimized neural architecture for entity mention detection and entity disambiguation that models the surrounding context on different levels of granularity.
SOTA for Entity Linking on WebQSP-WD
We propose a practical model for named entity recognition (NER) that combines word and character-level information with a specific learned representation of the prefixes and suffixes of the word.
This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets".
We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI).
This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.
Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions.