26 citations · 32 across the 5 of their papers we have counts for
11 papers · 1 filter
NTULM: Enriching Social Media Text Representations with Non-Textual Units
Jinning Li, Shubhanshu Mishra, Ahmed El-Kishky +2
On social media, additional context is often present in the form of annotations and meta-data such as the post's author, mentions, Hashtags, and hyperlinks. We refer to these annot…
CTM -- A Model for Large-Scale Multi-View Tweet Topic Classification
Vivek Kulkarni, Kenny Leung, Aria Haghighi
Automatically associating social media posts with topics is an important prerequisite for effective search and recommendation on many social media platforms. However, topic classif…
LMSOC: An Approach for Socially Sensitive Pretraining
Vivek Kulkarni, Shubhanshu Mishra, Aria Haghighi
While large-scale pretrained language models have been shown to learn effective linguistic representations for many NLP tasks, there remain many real-world contextual aspects of la…
What Should I Ask? Using Conversationally Informative Rewards for Goal-Oriented Visual Dialog
Pushkar Shukla, Carlos Elmadjian, Richika Sharan +3
The ability to engage in goal-oriented conversations has allowed humans to gain knowledge, reduce uncertainty, and perform tasks more efficiently. Artificial agents, however, are s…
MOHONE: Modeling Higher Order Network Effects in KnowledgeGraphs via Network Infused Embeddings
Hao Yu, Vivek Kulkarni, William Wang
Many knowledge graph embedding methods operate on triples and are therefore implicitly limited by a very local view of the entire knowledge graph. We present a new framework MOHONE…
DOLORES: Deep Contextualized Knowledge Graph Embeddings
Haoyu Wang, Vivek Kulkarni, William Yang Wang
We introduce a new method DOLORES for learning knowledge graph embeddings that effectively captures contextual cues and dependencies among entities and relations. First, we note th…