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Found 21 papers, 1 papers with code
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).
Minimizing Annotation Effort via Max-Volume Spectral Sampling
We address the annotation data bottleneck for sequence classification.
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.
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.
Interpolated Spectral NGram Language Models
Spectral models for learning weighted non-deterministic automata have nice theoretical and algorithmic properties.
Local String Transduction as Sequence Labeling
We show that the general problem of string transduction can be reduced to the problem of sequence labeling.
Arc-Standard Spinal Parsing with Stack-LSTMs
We present a neural transition-based parser for spinal trees, a dependency representation of constituent trees.
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.
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.
Language Processing Infrastructure in the XLike Project
This paper presents the linguistic analysis tools and its infrastructure developed within the XLike project.
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.
Joint Arc-factored Parsing of Syntactic and Semantic Dependencies
For the syntactic part, we define a standard arc-factored dependency model that predicts the full syntactic tree.
