Predicts whether online discussions will be productive or not.
problem Identifying wasteful discussions in group settings.
method Analyze conversational dynamics and linguistic cues.
result Linguistic cues and conversational patterns predict discussion productivity.
Paper addresses shortcomings in pointer generator networks for summarization.
problem Extractive summaries and factual inaccuracies in generated text.
method Appends traditional linguistic information to teach networks on text structure.
result Feasibility and potential of additional cues for improved generation.
Interpersonal relations are fickle, with close friendships often dissolving into enmity. In this work, we explore linguistic cues that presage such transitions by studying dyadic interactions in an online strategy game where players form alliances and break those alliances through betrayal. We characterize friendships …
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.
AdvReg improves VQA models but introduces instability and bias issues.
problem VQA models over-rely on linguistic biases, ignoring visual context.
method Adversarial regularization to encourage bias-free question representations.
result AdvReg yields side-effects like unstable gradients and reduced performance on in-domain examples.
Robotics improves by using image search to solve new tasks.
problem Generalization in robotics.
method Combining visual and textual information to demarcate intended word meaning.
result Our approach leads to improved results compared to Google searches, treating the problem of polysemes.
Method identifies credible medical statements from online health communities.
problem Inaccuracies and misinformation in user-generated medical resources.
method Probabilistic graphical model using linguistic cues and expert supervision.
result Successfully extracts rare or unknown side-effects of medical drugs.
Study reveals DNNs prefer easy-to-learn cues over essential ones in image recognition.
problem DNNs learn easy-to-learn features that aren't essential to the task.
method WCST-ML training setup with shortcut cues on synthetic and face datasets.
result DNNs converge to solutions focusing on preferred cues, leading to flat minima.
Computational model uncovers linguistic universals.
problem Manual processing of linguistic typology by linguists is time-consuming and leaves key universals unexplored.
method Presented a computational model to identify known and new linguistic universals.
result The model successfully identifies known universals and uncovers new ones.
ATD measures language distance using neural models, recovering linguistic groupings.
problem Lack of a unified quantitative measure for cross-linguistic distance.
method Pretrained multilingual language models, attention mechanisms, optimal transport.
result ATD quantifies representational distance between languages, recovering linguistic groupings.
Expands sparse disparity cues from LiDAR to improve stereo matching performance.
problem Improving stereo estimation performance with limited dense data.
method Proposes a sparsity expansion technique to enhance local features from sparse disparity cues.
result Significantly boosts stereo algorithms with sparse cues, outperforming previous methods.
Study shows adding noise to training data improves speech synthesis system's performance under noisy test conditions.
problem Impact of noisy linguistic features on neural network-based speech synthesis systems.
method Comparison of systems using ideal and corrupted linguistic features in training and test sets.
result Adding noise to training data can regularize the model and improve performance under noisy test conditions.
Automatically differentiable estimation for BLP model reduces bias in demand estimation.
problem Estimating the BLP model with reduced bias and improved performance.
method Phrasing BLP as an automatically differentiable moment function, using CUE for estimation, and incorporating MCMC credible intervals.
result CUE estimation shows lower bias but higher MAE compared to 2S-GMM, with MCMC providing closest empirical coverage.
A new method extracts linguistic objects from text using CNNs.
problem Lack of interpretability in deep learning models for text.
method Weighted extension of Text Deconvolution Saliency (wTDS) measure.
result Extracts interpretable linguistic objects from text.
GTI network learns linguistic features for multi-task sequence tagging.
problem Improving neural model performance on multi-task sequence tagging without explicit features.
method GTI network with neural gate modules to learn relations between tasks.
result GTI network outperforms baselines on chunking and NER tasks.
Proposes a method to interpret linguistic data models using parse trees and least-squares scores.
problem Interpreting trained classification models in linguistic data sets.
method Assigns least-squares based importance scores to words in a sentence using syntactic constituency structure and relates them to the Banzhaf value in coalitional game theory.
result Demonstrates the effectiveness of the proposed method in aiding interpretability and diagnostics for language models.
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.
The paper analyzes how CNNs interpret NLP tasks and identify linguistic features.
problem Understanding how CNNs capture linguistic features in NLP tasks.
method Visualization techniques and error analysis to interpret CNNs.
result Identified how CNNs capture different linguistic features and their impact on model performance.
The starting point of this article is the question "How to retrieve fingerprints of rhythm in written texts?" We address this problem in the case of Brazilian and European Portuguese. These two dialects of Modern Portuguese share the same lexicon and most of the sentences they produce are superficially identical. Yet t…
New method separates music vocals from accompaniment without labeled data.
problem Separating music sources without isolated recordings.
method Bootstrapping deep model using primitive auditory cues.
result Trained deep model separates vocals from accompaniment in unlabeled music.
Study investigates how simple speech sounds can form abstract categories.
problem How do abstract categories like phonemes emerge from speech exposure?
method Used modeling techniques to test Memory-Based Learning and Error-Correction Learning.
result Error-Correction Learning models can learn abstractions, identifying phone inventory and grouping.
Study shows how socioeconomic status influences language use on Twitter.
problem Global variability of linguistic patterns due to socioeconomic factors.
method Multivariate analysis of French Twitter corpus and socioeconomic data.
result People with higher socioeconomic status use more standard language.
New model combines stats and grammar, proving key property.
problem Creating a new model for linguistics and beyond.
method Introducing Markov substitute processes and proving their exponential family property.
result Markov substitute processes with a given support form an exponential family.
ALIEN improves uncertainty estimation of language models by refining entropy-based methods.
problem Overconfidence in uncertainty estimation for language models, especially for difficult inputs.
method ALIEN refines entropy-based uncertainty by aligning it with prediction reliability, using a lightweight uncertainty head.
result ALIEN consistently outperforms strong baselines in detecting incorrect predictions and achieving the lowest calibration error.
Linguistic calibration improves long-form text confidence.
problem LMs hallucinate, leading to suboptimal decisions.
method Defining linguistic calibration, training framework, reinforcement learning.
result Llama 2 7B is significantly more calibrated than baselines.
The abstract discusses parallels between Galois theory and Stone-Weierstrass theorem in various fields.
problem Connecting distinguishing power and expressive power in different fields.
method Elementary theorem connecting distinguishing power and expressive power.
result Foundational principle in linguistics linking distinguishing power and expressive power.
BERT captures linguistic features in separate semantic and syntactic subspaces.
problem Understanding how transformer models like BERT represent linguistic features internally.
method Qualitative and quantitative investigations of BERT's internal representations.
result Evidence of a fine-grained geometric representation of word senses and syntactic representations.
Study evaluates natural language models' ability to generalize across tasks.
problem Natural language models struggle with generalizing to new tasks.
method Empirical evaluation of state-of-the-art models using new metrics.
result Models require extensive in-domain training and are prone to forgetting.
ContextBench benchmarks methods for generating linguistically fluent inputs that activate specific latent features in language models.
problem Identifying inputs that trigger specific behaviours or latent features in language models.
method Context modification and benchmarking methods like Evolutionary Prompt Optimisation (EPO) with LLM-assistance and diffusion model inpainting.
result Enhanced methods achieve state-of-the-art performance in balancing elicitation effectiveness and fluency.
Multimodal analysis assesses job interview performance and provides feedback.
problem Assessing candidate performance in interviews for professional roles.
method Multimodal analytical framework using video, audio, and text data.
result The proposed methodology achieved promising results in predicting behavioral cues.
Exponential neural networks store many patterns, mapping cues to targets.
problem Storing many patterns in a neural network efficiently and accurately.
method Introduced an exponential neural network with multiple layers, each storing a dataset.
result The network can store an exponential number of patterns, and it generalizes well to unseen data.
Enhances SSL methods with depth cues for better image understanding.
problem Lack of depth cues in 2D image pixel maps limits SSL performance.
method Integrates depth signals from a pretrained monocular RGB-to-depth model into contrastive learning frameworks.
result Improves SSL methods' robustness and generalization with depth signals.
Neural stethoscopes improve and de-bias deep learning models in predicting block tower stability.
problem Improving and de-biasing deep learning models for predicting block tower stability.
method Introducing neural stethoscopes as a framework for quantifying and promoting/de-promoting feature importance in deep neural networks.
result Neural stethoscopes improve prediction accuracy from 51% to 90% and de-bias models from 66% to 88%.
AR-GANs learn depth and DoF from unlabeled images using aperture rendering and focus cues.
problem Learning depth and DoF from unlabeled natural images with diverse viewpoints and shapes.
method Aperture rendering and focus cues to learn depth and DoF from unlabeled images.
result AR-GANs effectively learn depth and DoF from various datasets, including flower, bird, and face images.
Investigates neural TTS systems for Japanese and English.
problem Improving neural TTS systems for high-quality speech synthesis.
method Comparative study of neural sequence-to-sequence TTS vs. DNN pipeline TTS, varying model architecture, parameter size, and language.
result A neural sequence-to-sequence TTS system requires sufficient model parameters and a powerful encoder for high-quality speech synthesis.
Enhances speech quality in noisy environments using symbolic sequential modeling.
problem Improving speech quality in noisy conditions.
method Incorporates symbolic sequential modeling into speech enhancement framework.
result Significant improvement in speech quality metrics (PESQ, STOI) on TIMIT dataset.
This work improves medical image segmentation with limited annotations using contrastive learning.
problem Lack of labeled data for medical image segmentation.
method Contrastive learning framework for semi-supervised segmentation with domain-specific and problem-specific cues.
result Significant improvements in segmentation performance compared to other methods.
A fast kernel-based measure for sparse linguistic expressions.
problem Efficiently measuring co-occurrence in sparse linguistic data.
method Derives PHSIC from HSIC, estimates it linearly, and uses various kernels.
result Empirically, PHSIC outperforms PMI in accuracy and learning speed.
Framework improves CT image segmentation robustness with domain-specific cues.
problem Challenges in CT image segmentation by deep learning models.
method Combines domain-specific preprocessing and augmentation with CNN architectures.
result Framework stabilizes prediction performance on varying CT volumes.
Paper uses motion cues to learn features for object detection.
problem Learning effective visual representations for object detection.
method Unsupervised motion-based segmentation of video frames to train a convolutional network.
result The learned representation significantly outperforms previous unsupervised approaches in object detection, especially in limited training data scenarios.
Paper tackles deconfounding age effects in dementia detection models.
problem Dementia detection models are affected by age, leading to potential non-generalizable accuracies.
method Proposes fair representation learning to learn age-invariant representations.
result Best models compromise accuracy by only 2.56% and 1.54% on clinical datasets.
Expanding spoken language understanding to handle complex entities and intents.
problem Handling compound entities and intents in spoken language understanding.
method Introducing a domain-agnostic shallow parser that handles linguistic coordination, learning domain-independent and slot-independent features.
result The model learns to segment conjunct boundaries of various phrasal categories and improves generalization across different slot types using adversarial training.
Qwant Research improves clinical case matching and information retrieval.
problem Matching and retrieving relevant clinical cases and discussions.
method Approach based on language models and preprocessings, information extraction system using neural networks and linguistic analysis.
result Very encouraging results in information extraction accuracy.
Paper proposes MMD-Sense-Analysis for detecting word sense shifts.
problem Detecting and interpreting shifts in word meanings over time.
method Leverages Maximum Mean Discrepancy (MMD) to identify and explain word sense changes.
result Demonstrates effectiveness of MMD-Sense-Analysis through empirical results.
Co-PLNet combines point and line predictions to improve wireframe parsing accuracy and efficiency.
problem Separate line and point predictions lead to inconsistent wireframes.
method Co-PLNet uses a Point-Line Prompt Encoder to convert early point detections into spatial prompts, which guide line refinement.
result Co-PLNet achieves better accuracy and robustness in wireframe parsing compared to existing methods.
HMN detects fake faces using memory networks and visual cues.
problem Detecting fake faces from unseen manipulation types.
method Hierarchical Memory Network (HMN) architecture.
result Superior performance in fake and fraudulent face detection.
Machine learning assesses group collaboration in classrooms.
problem Assess student group collaboration in large classrooms.
method Deep learning models using Mixup data augmentation and ordinal-cross-entropy loss function.
result Improved assessment of group collaboration quality.
VCML learns concepts and metaconcepts from images and questions.
problem Learning concepts and metaconcepts from visual data.
method Bidirectional connection between visual concepts and metaconcepts.
result VCML can generalize from limited data and noisy inputs.