Model predicts upcoming discourse referents using linguistic and script knowledge.
problem Predicting upcoming discourse referents based on linguistic knowledge.
method Built a computational model that predicts referents using linguistic knowledge and scripts.
result Script knowledge significantly improves model estimates of human predictions.
A new neural network models discourse relations with latent variables.
problem Jointly modeling discourse relations and word sequences.
method Latent variable recurrent neural network for discourse relations.
result Model outperforms state-of-the-art alternatives on discourse classification tasks.
Multiresolution RNN improves dialogue response generation.
problem Generating relevant and on-topic responses in dialogue systems.
method Introducing a multiresolution recurrent neural network that models natural language generation as two parallel sequences.
result The model outperforms competing approaches in dialogue response generation on the Ubuntu domain and appears more relevant on Twitter.
HDSF detects fake news by learning hierarchical discourse-level structures.
problem Detecting fake news articles with minimal annotated corpora.
method Automatically learns and constructs discourse-level structures for fake and real news articles.
result Real and fake news present substantial differences in hierarchical discourse-level structures.
Paper proposes a new method for sentence embeddings using weighted word vectors.
problem Improving sentence embeddings for natural language processing tasks.
method A simple sentence embedding method using weighted average of word vectors followed by soft projection.
result Demonstrates effectiveness on clinical semantic textual similarity task.
Improved text generation with discourse-aware soft prompts.
problem Efficient fine-tuning methods don't generalize across all generation tasks.
method Hierarchical blocking and attention sparsity on prefix parameters.
result Structured design of prefix parameters yields more coherent generations.
System detects financial news temporality combining NLP and ML.
problem Separate context from predictions in financial news.
method Combines NLP and ML, extracts dominant tenses.
result High detection precision compared to baseline.
This work speeds up unsupervised sentence learning using paragraph coherence.
problem Training fast unsupervised sentence encoders.
method Discourse-based objective function for neural network training.
result Models trained with this method are faster and perform well.
Word embeddings reveal multiple senses, which can be recovered using sparse coding.
problem Understanding word senses in polysemous words.
method Sparse coding of word embeddings to recover multiple senses.
result Sparse coding can approximate multiple word senses, with each sense associated with a discourse atom.
This study identifies sentence relationships in legal transcripts.
problem Improving understanding of legal case proceedings through sentence relationships.
method Combining machine learning and rule-based approach to classify sentence relationships.
result First study to use discourse relationships for legal court case transcripts.
New language models improve document coherence.
problem Existing language models fail to account for discourse structure.
method Introduced Document-Context Language Models (DCLM) using multi-level recurrent neural networks.
result DCLM models yield better document coherence than word-level models.
Adversarial model improves implicit relation classification without explicit connectives.
problem Lack of explicit connectives makes implicit discourse relation classification challenging.
method Feature imitation framework with adversarial training.
result State-of-the-art performance on PDTB benchmark.
Proposes a new method for conversational agents using deep learning.
problem Building coherent and non-monotonous conversational agents with proper discourse and coverage.
method End-to-end multi-stream deep learning architecture leveraging contextual and syntactic information.
result Significantly improved next sentence prediction task.
The study detects deceptive language in business communication using AI.
problem Deceptive language in business communication.
method Combining classical rhetoric, communication psychology, and linguistic theory with computational textual analysis and transformer models.
result Detection accuracies of over 99% achieved in controlled settings.
New approach to topic modelling with covariates for large text corpora.
problem Complex topic modelling in large text corpora.
method Combining convex NMF with regression for tractable estimation.
result Faster, interpretable, and better inferential justification than generative models.
Research finds correlations between Bitcoin online discourse and price/volume movements.
problem Mapping sentiment to Bitcoin price and volume movements.
method Collected and analyzed data from Bitcointalk.org, news sources, and Reddit communities.
result Weak to moderate correlations between online sentiment and Bitcoin price/volume movements.
Deep model captures interactions in online forums.
problem Modeling discursive interactions in online forums.
method Coupled Distributed Topics model with deep architecture and GPU-based inference.
result Model outperforms existing methods in online discourse analysis.
Bitcoin price prediction models fail to outperform a simple 'today's price' baseline, especially at longer horizons.
problem Lack of robust models that consistently outperform a naive price predictor at various horizons.
method Surveyed peer-reviewed papers, categorized by evaluation methodology, contrasted with social media discourse, and proposed methodological standards.
result No peer-reviewed study has shown robust superiority over the naive baseline across multiple market regimes at short-to-medium horizons.
Study examines active travel in Chicago communities, revealing mixed perceptions.
problem Transport disadvantage and lack of active mobility in underserved communities.
method Focus groups, qualitative discourse analysis, quantitative text-mining (topic modeling, sentiment analysis).
result Residents view active travel as both necessity and symbol of privilege, influenced by local culture.
The paper refines the discourse on interpretability of machine learning models.
problem The ambiguity and diversity of motivations for interpretability in machine learning models.
method Examining motivations, model properties, and techniques thought to confer interpretability.
result Identifies transparency and post-hoc explanations as competing notions of interpretability.
Paper discusses ethical norms for machine learning to prevent misuse.
problem Harmful misuse of machine learning applications.
method Proposes review parameters for ethical framework.
result Ethical guidelines for sharing sensitive machine learning information.
The study redefines algorithmic fairness as a sociotechnical concept.
problem Systemic discrimination in automated decision-making.
method Literature review and sociotechnical analysis.
result Algorithmic fairness should be viewed through a sociotechnical lens.
Designs ranking models to consider long-term consequences, improving online discourse.
problem Ranking models fail to foresee long-term negative impacts.
method Introduces Markov decision processes and weighted sampling for optimal consequential rankings; develops gradient-based algorithm for practical implementation.
result Optimal consequential rankings can be approximated efficiently using parameterized models.
Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.
problem Challenges in handling semantic and hierarchical structure in knowledge areas.
method Introduces a novel learner model that exploits semantic relatedness between knowledge components using a Wikipedia link graph.
result Achieves statistically significant improvements in predictive performance for educational engagement.
Enhances transfer learning with semantic reasoning for robust predictions.
problem Improving robustness of transfer learning models.
method Integrates semantic representations for better knowledge transfer.
result Demonstrated robustness in bus delay and air quality forecasting.
IdBench benchmarks semantic representations of identifiers, revealing strengths and weaknesses.
problem Evaluating semantic representations of identifiers in source code.
method Created a benchmark using developer ratings, evaluated natural language and source code embeddings, and compared lexical string distance functions.
result No single technique provides a satisfactory representation of semantic similarities, but ensemble models can improve performance.
SemGANs generate pixel-level accurate semantic images.
problem Generating semantic images with pixel-level accuracy.
method Semantic Generative Adversarial Networks (SemGANs).
result SemGANs outperform standard GANs in semantic image generation tasks.
SAM adds semantic attributes to language models for better interpretation and style variation.
problem Improving text interpretation and style variation in language models.
method SAM includes document attributes, scores them, and embeds them into the model's input space.
result SAM generates interpretable texts and shows superior performance on various datasets.
Graph-RISE learns image embeddings for ultra-fine-grained semantics.
problem Learning image representations for fine-grained semantics.
method Graph-regularized neural graph learning framework.
result Graph-RISE outperforms state-of-the-art on image classification and triplet ranking.
The paper proposes using semantic neighbors to decide when to stop learning.
problem Stopping point in iterative learning algorithms to avoid overfitting.
method Semantic stopping criteria based on inductive semantic stochastic methods.
result The proposed criteria detect stopping points leading to competitive generalization.
Low-dimensional vectors improve semantic understanding of music and language.
problem Noise in shared semantics due to individual brain biases.
method Jointly model multiple brains to learn low-dimensional vector embeddings.
result These embeddings outperform high-dimensional fMRI data in music and language classification.
LEAPS uses semantic models to improve reinforcement learning in diverse environments.
problem Generalizing and adapting to unseen environments in reinforcement learning.
method Hybrid model-based and model-free approach with a multi-target sub-policy and a Bayesian semantic model.
result LEAPS outperforms baselines in visual navigation tasks using diverse indoor scenes.
Mathematical theory explains neural network semantic development.
problem Understanding how neural networks acquire and organize abstract knowledge.
method Mathematical analysis of deep linear networks.
result Exact solutions reveal principles of semantic development.
Paper examines two methods for FX market volatility modeling.
problem FX market volatility modeling problem.
method Classical econometric GCH and mathematical approaches (SSA, dynamical systems stability analysis).
result Both mathematical tools show promising results in FX market volatility modeling.
New Bayes' theorem optimizes semantic channels for machine learning.
problem Class imbalance and semantic meaning evolution in natural language.
method Convert Shannon's channel to semantic channel using third kind of Bayes' theorem.
result CM algorithm explains natural language evolution and improves predictive models.
The paper shows how integrating categorical semantics can enhance unsupervised domain translation.
problem Improving unsupervised domain translation between perceptually different domains.
method Learning invariant categorical semantic features in an unsupervised manner and conditioning them on the style encoder.
result Conditioning the style encoder on learned categorical semantics improves translation and stylization.
Proposes a method to generate semantically meaningful adversarial examples.
problem Challenges in creating semantically meaningful adversarial examples.
method Captures semantics via manifold learning, perturbs using Gram-Schmidt process, and imposes adversarial constraints.
result Effectively generates adversarial examples that evade existing defenses.
Proposes a knowledge-guided semantic computing network for better neural network performance.
problem Difficulties in designing, interpreting, and predicting neural network performance.
method Knowledge-guided semantic tree and data-driven neural network modules.
result Improved performance with fewer training samples and lower complexity.
Paper surveys semantic segmentation for automated driving, highlighting challenges and solutions.
problem Semantic segmentation for automated driving.
method Taxonomic survey and empirical evaluation of semantic segmentation algorithms.
result Current semantic segmentation algorithms are not tailored for automated driving needs.
This work provides uncertainty intervals for semantic latent variables in disentangled latent spaces.
problem Challenges in providing meaningful uncertainty quantification for semantic information in disentangled latent spaces.
method Uses quantile regression to output heuristic uncertainty intervals, calibrates these intervals to contain true latent values, and propagates them through the generator.
result Reliably communicates semantically meaningful, principled, and instance-adaptive uncertainty in image super-resolution and image completion.
Semantify-NN verifies neural network robustness against semantic perturbations.
problem Verifying robustness of neural networks against semantic adversarial attacks.
method Inserting semantic perturbation layers (SP-layers) into neural networks to verify robustness.
result Semantify-NN significantly improves robustness verification performance over ℓp-norm-based methods. New system preserves message meaning in wireless networks, improving data rate.
problem Efficiently transmitting message meaning in wireless networks.
method Modeling semantics as hidden random variables, using Information Bottleneck for compression.
result 20 dB SNR improvement for semantic communication.
New diffusion models improve counterfactual image generation with semantic control.
problem Challenges in preserving identity, maintaining quality, and ensuring causal model faithfulness in counterfactual image generation.
method Integrates semantic representations into diffusion models through Pearlian causality, introducing spatial, semantic, and dynamic abduction.
result Demonstrates high-level semantic identity preservation and principled trade-offs between faithful causal control and identity preservation.
New approach uses SPG for semantic communication without a known channel model.
problem Designing efficient semantic communication systems without a known channel model.
method Applying Stochastic Policy Gradient (SPG) for reinforcement learning.
result Achieves comparable performance to model-aware approaches with a decreased convergence rate.
Service robots learn new tasks more efficiently with ISI, improving query performance and reducing training time.
problem Incremental learning of semantic concepts in multi-relational embeddings for service robots.
method Incremental Semantic Initialization (ISI) that allows new semantic concepts to be initialized in relation to previously learned embeddings.
result ISI improves immediate query performance by 41.4% and reduces the number of epochs to approach model convergence by 78.2%.
Probabilistic inpainting learns multiple plausible images from missing data.
problem Generating multiple plausible images from missing data in images.
method Building a PixelCNN model that learns a distribution of images conditioned on visible pixels.
result The method produces diverse and realistic inpaintings.
Unsupervised scheme ranks sentences in text documents based on semantic importance.
problem Ranking sentences in text documents without labeled data.
method Extracts essential words and phrases, constructs semantic phrase and sentence graphs, applies PageRank, combines scores, and optimizes for topic diversity.
result SSR outperforms individual judges and compares favorably with combined rankings on benchmarks.
SPAT improves adversarial robustness by preserving semantics in adversarial training.
problem Adversarial examples often have different semantics than original data, introducing unintended biases.
method Semantics-preserving adversarial training (SPAT) that encourages pixel perturbation shared among all classes.
result SPAT improves adversarial robustness and achieves state-of-the-art results in CIFAR-10 and CIFAR-100.