Search Results for author:
Found 24 papers, 6 papers with code
Fine-grained Entity Typing without Knowledge Base
Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision.
Satori: Towards Proactive AR Assistant with Belief-Desire-Intention User Modeling
Augmented Reality assistance are increasingly popular for supporting users with tasks like assembly and cooking.
ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality
Text presented in augmented reality provides in-situ, real-time information for users.
Reconfigurable AI Modules Aided Channel Estimation and MIMO Detection
Deep learning (DL) based channel estimation (CE) and multiple input and multiple output detection (MIMODet), as two separate research topics, have provided convinced evidence to demonstrate the effectiveness and robustness of artificial intelligence (AI) for receiver design.
Language Model Detoxification in Dialogue with Contextualized Stance Control
To reduce the toxic degeneration in a pretrained Language Model (LM), previous work on Language Model detoxification has focused on reducing the toxicity of the generation itself (self-toxicity) without consideration of the context.
Explanations from Large Language Models Make Small Reasoners Better
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations.
Controllable Dialogue Simulation with In-Context Learning
Experimental results on the MultiWOZ dataset demonstrate that training a model on the simulated dialogues leads to even better performance than using the same amount of human-generated dialogues under the challenging low-resource settings, with as few as 85 dialogues as a seed.
Soft MIMO Detection Using Marginal Posterior Probability Statistics
Soft demodulation of received symbols into bit log-likelihood ratios (LLRs) is at the very heart of multiple-input-multiple-output (MIMO) detection.
Limitations of Language Models in Arithmetic and Symbolic Induction
LMs with tutor is able to deliver 100% accuracy in situations of OOD and repeating symbols, shedding new insights on the boundary of large LMs in induction.
Controllable Natural Language Generation with Contrastive Prefixes
We propose a novel supervised method and also an unsupervised method to train the prefixes for single-aspect control while the combination of these two methods can achieve multi-aspect control.
Lifelong Learning of Hate Speech Classification on Social Media
In this work, we propose lifelong learning of hate speech classification on social media.
Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network
Deep learning, accounting for the use of an elaborate neural network, has recently been developed as an efficient and powerful tool to solve diverse problems in physics and other sciences.
Towards Understanding Gender Bias in Relation Extraction
We use WikiGenderBias to evaluate systems for bias and find that NRE systems exhibit gender biased predictions and lay groundwork for future evaluation of bias in NRE.
A Benchmark Dataset for Learning to Intervene in Online Hate Speech
In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.
Learning to Decipher Hate Symbols
Furthermore, we propose a novel Variational Decipher and show how it can generalize better to unseen hate symbols in a more challenging testing setting.
A Survey on Natural Language Processing for Fake News Detection
We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.
Hierarchical CVAE for Fine-Grained Hate Speech Classification
Existing work on automated hate speech detection typically focuses on binary classification or on differentiating among a small set of categories.
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection
Hate speech detection is a critical, yet challenging problem in Natural Language Processing (NLP).
Clustering and Community Detection with Imbalanced Clusters
Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present.
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs
We propose a non-parametric anomaly detection algorithm for high dimensional data.
Learning Efficient Anomaly Detectors from $K$-NN Graphs
We propose a non-parametric anomaly detection algorithm for high dimensional data.
Efficient Minimax Signal Detection on Graphs
Several problems such as network intrusion, community detection, and disease outbreak can be described by observations attributed to nodes or edges of a graph.
A Rank-SVM Approach to Anomaly Detection
The resulting anomaly detector is shown to be asymptotically optimal and adaptive in that for any false alarm rate alpha, its decision region converges to the alpha-percentile level set of the unknown underlying density.
Spectral Clustering with Imbalanced Data
Spectral clustering is sensitive to how graphs are constructed from data particularly when proximal and imbalanced clusters are present.
