Search Results for author:
Found 13 papers, 5 papers with code
Quantifying the Effects of COVID-19 on Restaurant Reviews
As of now, however, it is not clear how and to what extent the pandemic has affected restaurant reviews, an analysis of which could potentially inform policies for addressing this ongoing situation.
Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance
In this work, we explore a novel approach for supporting satellite-based flood index insurance by extracting high-resolution spatio-temporal information from news media.
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding
In this paper, we propose such a benchmark, named WALNUT (semi-WeAkly supervised Learning for Natural language Understanding Testbed), to advocate and facilitate research on weak supervision for NLU.
Self-Training with Weak Supervision
In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task.
Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only
To improve performance without extra annotations, we create artificial training documents in the target language through machine translation and train mBERT jointly for the source (English) and target language.
Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher
In this work, we propose a cross-lingual teacher-student method, CLTS, that generates "weak" supervision in the target language using minimal cross-lingual resources, in the form of a small number of word translations.
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Can one build a knowledge graph (KG) for all products in the world?
TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories
Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce.
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health
In many review classification applications, a fine-grained analysis of the reviews is desirable, because different segments (e. g., sentences) of a review may focus on different aspects of the entity in question.
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training
In this work, we consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words (i. e., weakly positive indicators) for the aspects of interest.
Training Neural Networks for Aspect Extraction Using Descriptive Keywords Only
In this work, we propose a weakly supervised approach for training neural networks for aspect extraction in cases where only a small set of seed words, i. e., keywords that describe an aspect, are available.
Item Recommendation with Variational Autoencoders and Heterogenous Priors
In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation of implicit feedback.
