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Found 20 papers, 2 papers with code
Minimizing Annotation Effort via Max-Volume Spectral Sampling
We address the annotation data bottleneck for sequence classification.
A comparison between CNNs and WFAs for Sequence Classification
We compare a classical CNN architecture for sequence classification involving several convolutional and max-pooling layers against a simple model based on weighted finite state automata (WFA).
Analyzing Text Representations by Measuring Task Alignment
Our experiments on text classification validate our hypothesis by showing that task alignment can explain the classification performance of a given representation.
Are Deep Sequence Classifiers Good at Non-Trivial Generalization?
We focus on sparse sequence classification, that is problems in which the target class is rare and compare three deep learning sequence classification models.
Analyzing Text Representations under Tight Annotation Budgets: Measuring Structural Alignment
That is why the ability to train models with limited annotation budgets is of great importance.
Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing
Prior work in semantic parsing has shown that conventional seq2seq models fail at compositional generalization tasks.
Measuring Alignment Bias in Neural Seq2Seq Semantic Parsers
Prior to deep learning the semantic parsing community has been interested in understanding and modeling the range of possible word alignments between natural language sentences and their corresponding meaning representations.
Interpolated Spectral NGram Language Models
Spectral models for learning weighted non-deterministic automata have nice theoretical and algorithmic properties.
Prepositional Phrase Attachment over Word Embedding Products
We present a low-rank multi-linear model for the task of solving prepositional phrase attachment ambiguity (PP task).
A Maximum Matching Algorithm for Basis Selection in Spectral Learning
Spectral algorithms reduce the learning problem to the task of computing an SVD decomposition over a special type of matrix called the Hankel matrix.
InToEventS: An Interactive Toolkit for Discovering and Building Event Schemas
Event Schema Induction is the task of learning a representation of events (e. g., bombing) and the roles involved in them (e. g, victim and perpetrator).
Structured Prediction with Output Embeddings for Semantic Image Annotation
We address the task of annotating images with semantic tuples.
Tailoring Word Embeddings for Bilexical Predictions: An Experimental Comparison
We investigate the problem of inducing word embeddings that are tailored for a particular bilexical relation.
Unsupervised Spectral Learning of Finite State Transducers
Finite-State Transducers (FST) are a standard tool for modeling paired input-output sequences and are used in numerous applications, ranging from computational biology to natural language processing.
A Joint Model for 2D and 3D Pose Estimation from a Single Image
We introduce a novel approach to automatically recover 3D human pose from a single image.
Ranked #25 on
3D Human Pose Estimation
on HumanEva-I
