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Found 12 papers, 3 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.
Improving Synonym Recommendation Using Sentence Context
Traditional synonym recommendations often include ill-suited suggestions for writer’s specific contexts.
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.
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 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.
Measure Utility, Gain Trust: Practical Advice for XAI Researcher
However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference
Across 648 experiments and two datasets, we evaluate every commonly used causal inference method and identify their strengths and weaknesses to inform social media researchers seeking to use such methods, and guide future improvements.
Improved Forecasting of Cryptocurrency Price using Social Signals
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics.
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.
