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Found 22 papers, 2 papers with code
An in-depth look at Euclidean disk embeddings for structure preserving parsing
We address this gap for two structure generation tasks, namely dependency and semantic parsing.
Graph Guided Question Answer Generation for Procedural Question-Answering
The key technological enabler is a novel mechanism for automatic question-answer generation from procedural text which can ingest large amounts of textual instructions and produce exhaustive in-domain QA training data.
GePSAn: Generative Procedure Step Anticipation in Cooking Videos
While most previous work focus on the problem of data scarcity in procedural video datasets, another core challenge of future anticipation is how to account for multiple plausible future realizations in natural settings.
SAGE: Saliency-Guided Mixup with Optimal Rearrangements
For image classification, the most popular data augmentation techniques range from simple photometric and geometrical transformations, to more complex methods that use visual saliency to craft new training examples.
Visual Semantic Parsing: From Images to Abstract Meaning Representation
The success of scene graphs for visual scene understanding has brought attention to the benefits of abstracting a visual input (e. g., image) into a structured representation, where entities (people and objects) are nodes connected by edges specifying their relations.
Graph2Vid: Flow graph to Video Grounding for Weakly-supervised Multi-Step Localization
In this setup, we seek the optimal step ordering consistent with the procedure flow graph and a given video.
Uncertainty-based Cross-Modal Retrieval with Probabilistic Representations
Probabilistic embeddings have proven useful for capturing polysemous word meanings, as well as ambiguity in image matching.
Dependency parsing with structure preserving embeddings
We do so by casting dependency parsing as a tree embedding problem where we incorporate geometric properties of dependency trees in the form of training losses within a graph-based parser.
Probabilistic Multimodal Representation Learning
Learning multimodal representations is a requirement for many tasks such as image--caption retrieval.
How coherent are neural models of coherence?
Despite the recent advances in coherence modelling, most such models including state-of-the-art neural ones, are evaluated on either contrived proxy tasks such as the standard order discrimination benchmark, or tasks that require special expert annotation.
Accurate polyglot semantic parsing with DAG grammars
We significantly improve upon this work, by proposing a simpler architecture as well as more efficient training and inference algorithms that can always guarantee the well-formedness of the generated graphs.
VASTA: A Vision and Language-assisted Smartphone Task Automation System
We present VASTA, a novel vision and language-assisted Programming By Demonstration (PBD) system for smartphone task automation.
Investigating the Opacity of Verb-Noun Multiword Expression Usages in Context
This study investigates the supervised token-based identification of Multiword Expressions (MWEs).
Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus
We then propose a supervised approach to classify out-of-vocabulary terms according to these categories, drawing on features based on word embeddings, and linguistic knowledge of common properties of out-of-vocabulary terms.
Using Noun Similarity to Adapt an Acceptability Measure for Persian Light Verb Constructions
In particular, LVCs are formed semi-productively: often a semantically-general verb (such as take) combines with a number of semantically-similar nouns to form semantically-related LVCs, as in make a decision/choice/commitment.
