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Found 29 papers, 9 papers with code
Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling
Finally, we find that learning with stories shows a higher retention rate for non-native speakers in the follow-up assessment.
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings
We propose ConGraT(Contrastive Graph-Text pretraining), a general, self-supervised method for jointly learning separate representations of texts and nodes in a parent (or ``supervening'') graph, where each text is associated with one of the nodes.
PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits
Despite the many use cases for large language models (LLMs) in creating personalized chatbots, there has been limited research on evaluating the extent to which the behaviors of personalized LLMs accurately and consistently reflect specific personality traits.
Language Models Trained on Media Diets Can Predict Public Opinion
Public opinion reflects and shapes societal behavior, but the traditional survey-based tools to measure it are limited.
Redrawing attendance boundaries to promote racial and ethnic diversity in elementary schools
Most US school districts draw "attendance boundaries" to define catchment areas that assign students to schools near their homes, often recapitulating neighborhood demographic segregation in schools.
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.
CommunityLM: Probing Partisan Worldviews from Language Models
As political attitudes have diverged ideologically in the United States, political speech has diverged lingusitically.
End-to-End Zero-Shot Voice Conversion with Location-Variable Convolutions
Zero-shot voice conversion is becoming an increasingly popular research topic, as it promises the ability to transform speech to sound like any speaker.
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis
We release the dataset and make both the Stanza pipeline and BERTweet-based models available "off-the-shelf" for use in future Tweet NLP research.
Ranked #3 on
Dependency Parsing
on Tweebank
Topic Detection and Tracking with Time-Aware Document Embeddings
TDT systems aim to cluster a corpus of news articles by event, and in that context, stories that describe the same event are likely to have been written at around the same time.
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.
Are Visual Explanations Useful? A Case Study in Model-in-the-Loop Prediction
We present a randomized controlled trial for a model-in-the-loop regression task, with the goal of measuring the extent to which (1) good explanations of model predictions increase human accuracy, and (2) faulty explanations decrease human trust in the model.
Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types
Second, we ask non-narrative experts to annotate those clauses under Labov's sociolinguistic model of personal narratives (i. e., action, orientation, and evaluation clause types) and train a classifier that reaches 84. 7% F-score for the highest-agreed clauses.
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.
RadioTalk: a large-scale corpus of talk radio transcripts
We introduce RadioTalk, a corpus of speech recognition transcripts sampled from talk radio broadcasts in the United States between October of 2018 and March of 2019.
Learning Personas from Dialogue with Attentive Memory Networks
We introduce neural models to learn persona embeddings in a supervised character trope classification task.
Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies
In this paper, we use machine learning methods to construct emotional arcs in movies, calculate families of arcs, and demonstrate the ability for certain arcs to predict audience engagement.
Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands
Symbolic models capture linguistic structure but have not scaled successfully to handle the diverse language produced by untrained users.
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).
Enhanced Twitter Sentiment Classification Using Contextual Information
This combined classifier outperforms the purely linguistic classifier, showing that integrating the rich contextual information available on Twitter into sentiment classification is a promising direction of research.
Tweet Acts: A Speech Act Classifier for Twitter
We created a taxonomy of six speech acts for Twitter and proposed a set of semantic and syntactic features.
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.
A Semi-automatic Method for Efficient Detection of Stories on Social Media
In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter.
Digital Stylometry: Linking Profiles Across Social Networks
There is an ever growing number of users with accounts on multiple social media and networking sites.
