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Found 13 papers, 0 papers with code
TWEETSPIN: Fine-grained Propaganda Detection in Social Media Using Multi-View Representations
Our model relies on multi-view representations of the input tweet data to (a) extract different aspects of the input text including the context, entities, their relationships, and external knowledge; (b) model their mutual interplay; and (c) effectively speed up the learning process by requiring fewer training examples.
PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction
This study proposes a Prototype-based Multi-view Network (PROMINET) that incorporates semantic and structural information from email data.
M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using Protagonist's Mental Representations
To this end, we implement a computational model that leverages the protagonist's mental state information obtained from a pre-trained model trained on social commonsense knowledge and integrates their representations with contextual semantic embed-dings using a multi-feature fusion approach.
Lifelong Knowledge-Enriched Social Event Representation Learning
First, we investigate methods to incorporate pragmatic aspects into our social event embeddings by leveraging social commonsense knowledge.
Interpretable Multi-Modal Hate Speech Detection
With growing role of social media in shaping public opinions and beliefs across the world, there has been an increased attention to identify and counter the problem of hate speech on social media.
DAPPER: Learning Domain-Adapted Persona Representation Using Pretrained BERT and External Memory
In this paper, we present a model called DAPPER that can learn to embed persona from natural language and alleviate task or domain-specific data sparsity issues related to personas.
Video SemNet: Memory-Augmented Video Semantic Network
Stories are a very compelling medium to convey ideas, experiences, social and cultural values.
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model
Recently, generating adversarial examples has become an important means of measuring robustness of a deep learning model.
Learning Personas from Dialogue with Attentive Memory Networks
We introduce neural models to learn persona embeddings in a supervised character trope classification task.
Twitter Demographic Classification Using Deep Multi-modal Multi-task Learning
In this paper, we present a demographic classifier for gender, age, political orientation and location on Twitter.
Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder
The vector representations generated by our model are generic, and hence can be applied to a variety of tasks.
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
This paper describes our approach for the Detecting Stance in Tweets task (SemEval-2016 Task 6).
Automatic Detection and Categorization of Election-Related Tweets
With the rise in popularity of public social media and micro-blogging services, most notably Twitter, the people have found a venue to hear and be heard by their peers without an intermediary.
