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Found 20 papers, 8 papers with code
Jelly Bean World: A Testbed for Never-Ending Learning
Never-ending learning is a machine learning paradigm that aims to bridge this gap, with the goal of encouraging researchers to design machine learning systems that can learn to perform a wider variety of inter-related tasks in more complex environments.
Constrained Language Models Yield Few-Shot Semantic Parsers
We explore the use of large pretrained language models as few-shot semantic parsers.
Re-TACRED: Addressing Shortcomings of the TACRED Dataset
Finally, aside from our analysis we also release Re-TACRED, a new completely re-annotated version of the TACRED dataset that can be used to perform reliable evaluation of relation extraction models.
Contextual Parameter Generation for Universal Neural Machine Translation
We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation.
Active Learning amidst Logical Knowledge
Structured prediction is ubiquitous in applications of machine learning such as knowledge extraction and natural language processing.
Competence-based Curriculum Learning for Neural Machine Translation
In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance.
Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction
Relation extraction (RE) aims to predict a relation between a subject and an object in a sentence, while knowledge graph link prediction (KGLP) aims to predict a set of objects, O, given a subject and a relation from a knowledge graph.
When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems
Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows.
Agreement-based Learning
Model selection is a problem that has occupied machine learning researchers for a long time.
Learning from Imperfect Annotations
Many machine learning systems today are trained on large amounts of human-annotated data.
HyperDynamics: Generating Expert Dynamics Models by Observation
We propose HyperDynamics, a framework that conditions on an agent’s interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system.
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks
We propose HyperDynamics, a dynamics meta-learning framework that conditions on an agent's interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system.
Coarse-to-Fine Curriculum Learning
However, in machine learning, models are most often trained to solve the target tasks directly. Inspired by human learning, we propose a novel curriculum learning approach which decomposes challenging tasks into sequences of easier intermediate goals that are used to pre-train a model before tackling the target task.
Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention
We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers.
Value-Agnostic Conversational Semantic Parsing
Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses.
Learning from Imperfect Annotations: An End-to-End Approach
Many machine learning systems today are trained on large amounts of human-annotated data.
Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue
Standard conversational semantic parsing maps a complete user utterance into an executable program, after which the program is executed to respond to the user.
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion
Text-to-SQL parsers map natural language questions to programs that are executable over tables to generate answers, and are typically evaluated on large-scale datasets like Spider (Yu et al., 2018).
Guided K-best Selection for Semantic Parsing Annotation
Collecting data for conversational semantic parsing is a time-consuming and demanding process.
