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Found 25 papers, 10 papers with code
Entity and Evidence Guided Document-Level Relation Extraction
In this paper, we propose a novel framework E2GRE (Entity and Evidence Guided Relation Extraction) that jointly extracts relations and the underlying evidence sentences by using large pretrained language model (LM) as input encoder.
MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text
Material science synthesis procedures are a promising domain for scientific NLP, as proper modeling of these recipes could provide insight into new ways of creating materials.
Speech2rtMRI: Speech-Guided Diffusion Model for Real-time MRI Video of the Vocal Tract during Speech
Understanding speech production both visually and kinematically can inform second language learning system designs, as well as the creation of speaking characters in video games and animations.
Exploring Speech Foundation Models for Speaker Diarization in Child-Adult Dyadic Interactions
Speech foundation models, trained on vast datasets, have opened unique opportunities in addressing challenging low-resource speech understanding, such as child speech.
DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control
Precise arbitrary trajectory tracking for quadrotors is challenging due to unknown nonlinear dynamics, trajectory infeasibility, and actuation limits.
Deep Model Predictive Optimization
A major challenge in robotics is to design robust policies which enable complex and agile behaviors in the real world.
Audio-visual child-adult speaker classification in dyadic interactions
Building on the foundation of an audio-only child-adult speaker classification pipeline, we propose incorporating visual cues through active speaker detection and visual processing models.
Automatic Gradient Descent: Deep Learning without Hyperparameters
Automatic gradient descent trains both fully-connected and convolutional networks out-of-the-box and at ImageNet scale.
Augmenting Scientific Creativity with Retrieval across Knowledge Domains
Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas.
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning
It then uses the top trajectories as initialization for gradient descent and applies gradient updates to each of these trajectories to find the optimal action sequence.
Towards Cross-Cultural Analysis using Music Information Dynamics
The framework is based on an Music Information Dynamics model, a Variable Markov Oracle (VMO), and is extended with a variational representation learning of audio.
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks
The core of our algorithm is to introduce a novel variance reduction term to the gradient estimation when performing the task adaptation.
Multiple regression techniques for modeling dates of first performances of Shakespeare-era plays
In this study, we took a set of Shakespeare-era plays (181 plays from the period 1585--1610), added the best-guess dates for them from a standard reference work as metadata, and calculated a set of probabilities of individual words in these samples.
Accelerating Road Sign Ground Truth Construction with Knowledge Graph and Machine Learning
We show that our knowledge graph approach can reduce sign search space by 98. 9%.
Learning to extrapolate using continued fractions: Predicting the critical temperature of superconductor materials
We refer to $S$ as the training set and aim to identify a low-complexity mathematical model that can effectively approximate this target function for new instances $\mathbf{x}$.
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
In this paper, we propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems.
Ranked #6 on
Relation Extraction
on ReDocRED
Document-level Relation Extraction
Multi-Label Classification
+2
Entity and Evidence Guided Relation Extraction for DocRED
Document-level relation extraction is a challenging task which requires reasoning over multiple sentences in order to predict relations in a document.
Ranked #14 on
Relation Extraction
on DocRED
Improving Neural Language Generation with Spectrum Control
Recent Transformer-based models such as Transformer-XL and BERT have achieved huge success on various natural language processing tasks.
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences.
Relation Module for Non-Answerable Predictions on Reading Comprehension
We test the relation module on the SQuAD 2. 0 dataset using both the BiDAF and BERT models as baseline readers.
Relation Module for Non-answerable Prediction on Question Answering
In this paper, we aim to improve a MRC model's ability to determine whether a question has an answer in a given context (e. g. the recently proposed SQuAD 2. 0 task).
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text.
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Leveraging new data sources is a key step in accelerating the pace of materials design and discovery.
ParaNet - Using Dense Blocks for Early Inference
DenseNets have been shown to be a competitive model among recent convolutional network architectures.
Automatically Extracting Action Graphs from Materials Science Synthesis Procedures
In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds.
