Search Results for author:
Found 18 papers, 8 papers with code
Jellyfish: Networking Data Centers Randomly
We present Jellyfish, a high-capacity network interconnect, which, by adopting a random graph topology, yields itself naturally to incremental expansion.
Networking and Internet Architecture
Knowledge Refinement via Rule Selection
In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules.
Low-resource Deep Entity Resolution with Transfer and Active Learning
Recent adaptation of deep learning methods for ER mitigates the need for dataset-specific feature engineering by constructing distributed representations of entity records.
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching
Entity Matching (EM) is a core data cleaning task, aiming to identify different mentions of the same real-world entity.
Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context
With GTE-Table, we invent a new penalty based on the natural cell containment constraint of tables to train our table network aided by cell location predictions.
Learning Structured Representations of Entity Names using Active Learning and Weak Supervision
Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation.
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversational systems.
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
These LFs, in turn, have been used to generate a large amount of additional noisy labeled data, in a paradigm that is now commonly referred to as data programming.
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Our central insight is that using an informative subset of the dataset for model training runs involved in hyper-parameter optimization, allows us to find the optimal hyper-parameter configuration significantly faster.
Domain Representative Keywords Selection: A Probabilistic Approach
We propose a probabilistic approach to select a subset of a \textit{target domain representative keywords} from a candidate set, contrasting with a context domain.
Entity Set Co-Expansion in StackOverflow
Given a few seed entities of a certain type (e. g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds.
MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Training deep networks and tuning hyperparameters on large datasets is computationally intensive.
A Framework for Combining Entity Resolution and Query Answering in Knowledge Bases
We propose a new framework for combining entity resolution and query answering in knowledge bases (KBs) with tuple-generating dependencies (tgds) and equality-generating dependencies (egds) as rules.
Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations
Human-annotated labels and explanations are critical for training explainable NLP models.
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations.
Long-form Question Answering: An Iterative Planning-Retrieval-Generation Approach
From an extensive experiment on both an open domain and a technical domain QA dataset, we find that our model outperforms the state-of-the-art models on various textual and factual metrics for the LFQA task.
Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to classify new entity mentions into both seen and unseen types (i. e., those without seed entities).
