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Found 30 papers, 5 papers with code
Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned
Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e. g., law, healthcare, education, etc.
Understanding and Explicitly Measuring Linguistic and Stylistic Properties of Deception via Generation and Translation
We perform multi-dimensional evaluation of model performance on mimicking both the style and linguistic differences that distinguish news of different credibility using machine translation metrics and classification models.
Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph Transformers
The ability to anticipate technical expertise and capability evolution trends globally is essential for national and global security, especially in safety-critical domains like nuclear nonproliferation (NN) and rapidly emerging fields like artificial intelligence (AI).
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time.
Unsupervised Keyphrase Extraction via Interpretable Neural Networks
Keyphrase extraction aims at automatically extracting a list of "important" phrases representing the key concepts in a document.
Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community
Drawing causal conclusions from observational real-world data is a very much desired but challenging task.
Evaluating Deception Detection Model Robustness To Linguistic Variation
With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs.
Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages
Our analyses reveal a significant drop in performance when testing neural models on out-of-domain data and non-English languages that may be mitigated using diverse training data.
Evaluating Neural Model Robustness for Machine Comprehension
We evaluate neural model robustness to adversarial attacks using different types of linguistic unit perturbations {--} character and word, and propose a new method for strategic sentence-level perturbations.
Hokey Pokey Causal Discovery: Using Deep Learning Model Errors to Learn Causal Structure
Many existing algorithms for the discovery of causal structure from observational data rely on evaluating the conditional independence relationships among features to account for the effects of confounding.
Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks
We evaluate machine comprehension models' robustness to noise and adversarial attacks by performing novel perturbations at the character, word, and sentence level.
Improved Forecasting of Cryptocurrency Price using Social Signals
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics.
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian
This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages.
Ranked #2 on
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Predicting Foreign Language Usage from English-Only Social Media Posts
We rely on this corpus to build predictive models to infer non-English languages that users speak exclusively from their English tweets.
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources
In the age of social news, it is important to understand the types of reactions that are evoked from news sources with various levels of credibility.
Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
We evaluate the predictive power of models trained on varied text and image representations extracted from tweets.
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
We present an analytic study on the language of news media in the context of political fact-checking and fake news detection.
Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams
Language in social media is a dynamic system, constantly evolving and adapting, with words and concepts rapidly emerging, disappearing, and changing their meaning.
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter
In this work we build predictive models to classify 130 thousand news posts as suspicious or verified, and predict four sub-types of suspicious news {--} satire, hoaxes, clickbait and propaganda.
Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast
People around the globe respond to major real world events through social media.
