Convolutional neural networks win SemEval-2017 for scientific relation extraction.
problem Extracting relations between scientific concepts from scholarly articles.
method Convolutional neural network model for relation extraction.
result Ranked first in SemEval-2017 Task 10 for relation extraction in scientific articles.
Amobee's system won 3rd place in Twitter sentiment classification.
problem Sentiment detection on Twitter using deep learning.
method RNN models trained on a sentiment treebank, combined with classifiers.
result 3rd place in SemEval 2017 task 4, 5-label classification.
State-of-the-art Twitter sentiment analysis using CNNs and LSTMs.
problem Improving Twitter sentiment classification accuracy.
method Pre-trained word embeddings, distant supervision, fine-tuning, ensemble of CNNs and LSTMs.
result First rank on all five English subtasks of SemEval-2017.
Extract keyphrases and relations from scientific documents.
problem Understanding which publications describe which processes, tasks, and materials.
method Evaluated 26 submissions across 3 scenarios.
result Task and findings relevant for researchers and information extraction communities.
Amobee won 3rd and 1st place in SemEval 2018 sentiment classification tasks.
problem Sentiment classification in multiple languages.
method Training GRU-CNN model with word embeddings and stacking ensembles.
result 3rd and 1st place in valence ordinal classification sub-tasks in English and Spanish.
RoBERTa model detects counterfactual statements in text.
problem Detecting and extracting counterfactual statements from text.
method Used RoBERTa language representation model for both subtasks.
result RoBERTa achieved top performance in both subtasks at SemEval-2020.
Task focuses on fact checking in Q&A forums, improving over baseline systems.
problem Fact checking in community Q&A forums to distinguish factual from opinion.
method Two subtasks: distinguishing factual vs. opinion/advice/socializing, predicting answer truthfulness.
result Improved over baseline systems for both subtasks, but not for Subtask B.
Improved speech emotion recognition using pre-trained language models.
problem Challenging task of speech emotion recognition for natural human-machine interaction.
method Fine-tuning pre-trained language models for text emotion recognition, combining with speech emotion recognition.
result 73.5% accuracy in speech emotion recognition on a subset of IEMOCAP dataset.
Ranked second in fact-checking task, using DRR NN with embeddings.
problem Fact-checking questions in community forums.
method Deeply Regularized Residual Neural Network (DRR NN) with Universal Sentence Encoder embeddings, ensemble methods.
result Ranked second in fact-checking task.
Improved offensive language detection in tweets with multiple deep learning models.
problem Detecting offensive language in tweets using machine learning.
method Combination of multiple deep learning architectures for classification.
result Achieved macro-average F1-scores of 0.76, 0.68, 0.54 for different tasks.
Team QCRI-MIT detects hyperpartisan news with 72.9% accuracy.
problem Detecting hyperpartisan news from biased political content.
method Logistic regression model using engineered features from propaganda detection.
result Significant performance improvements with better feature pre-processing.
Paper tackles counterfactual sentence detection and evaluation.
problem Detect and evaluate counterfactual sentences in natural language.
method Used a BERT base model for classification and a hybrid BERT Multi-Layer Perceptron for sequence identification. Introduced cascaded linear inputs to improve performance.
result Achieved an F1 score of 85.00% in Task 1 and 83.90% in Task 2.
System classifies Twitter and Reddit posts' stance towards hidden rumour threads.
problem Classifying posts' stance towards hidden rumour threads.
method Used pre-trained deep bidirectional transformers (BERT) for stance classification.
result Reached F1 score of 61.67% on test data, 2nd place in competition.
ICML workshop on making machine learning models more understandable.
problem Making machine learning models more understandable to humans.
method Various presentations and discussions on interpretability techniques.
result Improved methods for explaining machine learning models.
Symposium on making machine learning models more understandable.
problem Making machine learning models more understandable.
method Not specified in the abstract.
result Not specified in the abstract.
We describe our language-independent unsupervised word sense induction system. This system only uses topic features to cluster different word senses in their global context topic space. Using unlabeled data, this system trains a latent Dirichlet allocation (LDA) topic model then uses it to infer the topics distribution…
Study categorizes and analyzes emotions in sexist tweets.
problem Lack of defined categories for sexism in NLP.
method Used a new dataset from SemEval-2018 to classify and analyze emotions in sexist tweets.
result Demonstrated the mental state and affectual state of users who tweet in different categories of sexism.
NIPS workshop focuses on ML for developing countries.
problem Addressing machine learning challenges in developing nations.
method Not specified in the abstract.
result Not specified in the abstract.
Tangent Works won GEFCom 2017 using automatic model building.
problem Forecasting time series with historical temperature shuffling.
method Automatic model building using Tangent Information Modeller (TIM) with historical temperature shuffling and decision on trend variable.
result Automated model building setup won the competition.
Study shows changes in information sharing between Bitcoin markets during 2017 crash.
problem Understanding information dynamics in Bitcoin markets during the 2017 crash.
method Analysis of high-frequency market-microstructure observables using information theoretic measures.
result Temporal changes in information sharing across markets, including predictability, memory, and synchronous coupling.
Interpretable semantic textual similarity (iSTS) task adds a crucial explanatory layer to pairwise sentence similarity. We address various components of this task: chunk level semantic alignment along with assignment of similarity type and score for aligned chunks with a novel system presented in this paper. We propose…
Y. Nikonorov completes a proof in a geometry paper.
problem Completing a proof in a geometry paper.
method Completing an argument from a previous proof.
result Proof of Theorem 2.5 in JGA 27 (2017) is now complete.
ES and FD gradients converge as optimization dimension grows.
problem Understanding the relationship between Evolution Strategies and Finite Differences gradients.
method Analyzing the convergence of gradients as the optimization dimension increases.
result ES and FD gradients converge as the dimension of the vector under optimization increases.
Survey of open problems in finite-dimensional integrable systems.
problem Open problems in finite-dimensional integrable systems.
method None specified; survey of existing open problems.
result Many open problems were identified from a conference.
Simple multilingual sentiment analysis framework outperforms existing methods.
problem Multilingual sentiment analysis in social media.
method A simple and easy-to-implement multilingual framework for sentiment classification.
result Outperforms existing methods in multiple languages, including SemEval, TASS, and SENTIPOLC.
Two algorithms find local minima faster in finite-sum and general stochastic optimization.
problem Finding local minima in finite-sum and general stochastic nonconvex optimization.
method Stochastic Nested Variance Reduction (SNVRG) + Neon2.
result Achieves better gradient complexity for convergence to (ε,εH)-second-order stationary points. Explains deep neural networks using new interpretation techniques.
problem Interpreting and understanding deep neural networks.
method Introduces new techniques for interpretation and practical applications.
result Efficient use of interpretation techniques on real data.
This paper covers the two approaches for sentiment analysis: i) lexicon based method; ii) machine learning method. We describe several techniques to implement these approaches and discuss how they can be adopted for sentiment classification of Twitter messages. We present a comparative study of different lexicon combin…
A new system detects audio replay attacks with high accuracy.
problem Detecting and preventing audio replay attacks in speaker verification systems.
method Proposes Attentive Filtering Network combining attention-based filtering and ResNet classifier.
result Achieves EER of 8.99% on ASVspoof 2017 Version 2.0 dataset.
Stable ResNet stabilizes gradients in deep networks.
problem Gradient vanishing and exploding in deep ResNet architectures.
method Introducing Stable ResNet architectures with gradient stabilization and infinite depth expressivity.
result Stable ResNet maintains gradient stability and expressivity in deep networks.
Improved neural network robustness against adversarial attacks.
problem Adversarial attacks on neural networks.
method Adversarial-trained Bayesian Neural Network (Adv-BNN).
result State-of-the-art performance improvement under strong attacks.
Forecast predicts US recession in 2017, global economic slowdown, and eventual growth.
problem Short-term economic forecast and potential recession in developed countries.
method Analysis of log-periodic oscillations in DJIA dynamics and historical economic cycles.
result Predicts a recession in the second half of 2017 for developed countries.
Study quantifies reproducibility of machine learning papers.
problem Lack of empirical reproducibility metrics in machine learning.
method Manual implementation of 255 papers from 1984-2017, analyzing features and results.
result Manual implementation revealed discrepancies between papers and their descriptions.
EWC uses quadratic penalties that may double-count earlier task data.
problem Catastrophic forgetting in neural networks.
method Extended derivation of EWC with multiple tasks.
result Quadratic penalties in EWC might double-count earlier task data.
A framework for multi-label sentiment analysis in 100 languages with dynamic weighting.
problem Cross-lingual sentiment analysis in multi-label settings with label imbalance.
method Dynamic weighting method, focal loss adaptation, optimal class-specific thresholds.
result State-of-the-art performance in 7 out of 9 metrics across 3 languages.
Paper proposes Experts Model for better emotion detection in tweets.
problem Estimating intensity of emotion in tweets.
method Inspired by Mixture of Experts (MoE) model, each expert learns different features.
result Our Experts Model stands at top-5 results in emotion detection.
Directly analyzes SGLD hitting times for stationary points, providing tighter bounds.
problem Analyzing the hitting time of SGLD to stationary points.
method Direct analysis using linear algebra and probability theory, avoiding complex Cheeger's constant bounds.
result Tighter bounds on hitting times compared to previous work, showing dimension-independent behavior under suitable conditions.
New estimator stabilizes higher-order influence functions for stable statistical inference.
problem Numerical instability in estimating inverse population Gram matrix.
method Proposes a new stabilized higher-order estimator without sample splitting.
result Stabilized estimator exhibits more stable performance and similar statistical guarantees.
Paper compares different spoofing detection methods for speech verification.
problem Detecting audio replay attacks in speech verification systems.
method GMM based methods, high level features extraction with simple classifier, deep learning frameworks.
result Deep learning approaches are efficient in changing acoustic conditions.
New method calibrates rough stochastic volatility models quickly.
problem Calibrating rough stochastic volatility models is expensive and time-consuming.
method Combines Levenberg-Marquardt with neural networks for fast calibration.
result Neural network approximates implied volatility map efficiently.
Deep ResNets with single-neuron hidden layers can approximate any function.
problem The challenge of universal approximation by deep neural networks.
method A ResNet architecture with one neuron per hidden layer in each module.
result ResNet with one-neuron hidden layers is a universal approximator.
New estimator stabilizes higher-order influence functions for bilinear forms.
problem Stability issues in estimating bilinear forms using higher-order influence functions.
method Proposes a new stabilized higher-order estimator for a class of bilinear forms without sample splitting.
result New estimator exhibits more stable finite-sample performance compared to the empirical higher-order estimator.
Hard to estimate L2-accurate scores without strong assumptions.
problem Estimating the score of unknown data distributions accurately.
method Reduction to generating samples and leveraging lattice-based cryptography hardness.
result Score estimation is computationally hard even with polynomial sample complexity.
Neural networks model future values in finance.
problem Modeling future values of financial portfolios.
method Deep learning with neural networks to parameterize future values, optimizing parameters.
result Obtained expected positive/negative exposures for specific financial products.
The Dynamic Pricing Challenge revealed varying algorithm performance across different market dynamics.
problem Complexity of pricing and learning in competitive markets.
method Participants submitted pricing and demand learning algorithms for numerical performance analysis in simulated environments.
result Algorithm performance varies significantly across different market dynamics.
New insights into neural network initialization and activation functions improve deep learning performance.
problem Inappropriate initialization and activation function selection can hinder deep neural network training.
method Theoretical analysis and quantitative results on weight initialization and activation functions.
result Random initialization at the edge of chaos improves information propagation in deep neural networks.
Iterated Amplification uses subproblem solutions to build training signals for complex tasks.
problem Learning complex tasks when humans can't directly evaluate performance.
method Progressively builds training signal by combining solutions to easier subproblems.
result Efficiently learns complex behaviors in algorithmic environments.
New model closes gap in understanding equivariant set functions.
problem Understanding universality of equivariant set functions.
method Proves PointNet not equivariant universal and introduces PointNetST.
result PointNetST is the simplest permutation equivariant universal model.