Search Results for author: Emmanouil Antonios Platanios

Found 19 papers, 7 papers with code

Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion

no code implementations ACL 2022 Chen Zhao, Yu Su, Adam Pauls, Emmanouil Antonios Platanios

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).

Domain Generalization SQL Parsing +1

Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue

no code implementations ACL 2022 Jiawei Zhou, Jason Eisner, Michael Newman, Emmanouil Antonios Platanios, Sam Thomson

Standard conversational semantic parsing maps a complete user utterance into an executable program, after which the program is executed to respond to the user.

Machine Translation Semantic Parsing +1

Value-Agnostic Conversational Semantic Parsing

no code implementations ACL 2021 Emmanouil Antonios Platanios, Adam Pauls, Subhro Roy, Yuchen Zhang, Alexander Kyte, Alan Guo, Sam Thomson, Jayant Krishnamurthy, Jason Wolfe, Jacob Andreas, Dan Klein

Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses.

Semantic Parsing

Coarse-to-Fine Curriculum Learning

no code implementations8 Jun 2021 Otilia Stretcu, Emmanouil Antonios Platanios, Tom M. Mitchell, Barnabás Póczos

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.

Re-TACRED: Addressing Shortcomings of the TACRED Dataset

1 code implementation16 Apr 2021 George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos

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.

Relation Extraction

HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks

no code implementations17 Mar 2021 Zhou Xian, Shamit Lal, Hsiao-Yu Tung, Emmanouil Antonios Platanios, Katerina Fragkiadaki

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.

Meta-Learning

HyperDynamics: Generating Expert Dynamics Models by Observation

no code implementations ICLR 2021 Zhou Xian, Shamit Lal, Hsiao-Yu Tung, Emmanouil Antonios Platanios, Katerina Fragkiadaki

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.

Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction

1 code implementation9 Dec 2020 George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos

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.

Link Prediction Multi-Task Learning +1

Learning from Imperfect Annotations

no code implementations7 Apr 2020 Emmanouil Antonios Platanios, Maruan Al-Shedivat, Eric Xing, Tom Mitchell

Many machine learning systems today are trained on large amounts of human-annotated data.

Ensemble Learning

Jelly Bean World: A Testbed for Never-Ending Learning

3 code implementations ICLR 2020 Emmanouil Antonios Platanios, Abulhair Saparov, Tom Mitchell

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.

Competence-based Curriculum Learning for Neural Machine Translation

1 code implementation NAACL 2019 Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, Tom M. Mitchell

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.

Machine Translation Translation

Contextual Parameter Generation for Universal Neural Machine Translation

1 code implementation EMNLP 2018 Emmanouil Antonios Platanios, Mrinmaya Sachan, Graham Neubig, Tom Mitchell

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.

Domain Adaptation Machine Translation +1

Agreement-based Learning

no code implementations4 Jun 2018 Emmanouil Antonios Platanios

Model selection is a problem that has occupied machine learning researchers for a long time.

Model Selection

Deep Graphs

no code implementations4 Jun 2018 Emmanouil Antonios Platanios, Alex Smola

We propose an algorithm for deep learning on networks and graphs.

Active Learning amidst Logical Knowledge

1 code implementation26 Sep 2017 Emmanouil Antonios Platanios, Ashish Kapoor, Eric Horvitz

Structured prediction is ubiquitous in applications of machine learning such as knowledge extraction and natural language processing.

Active Learning Structured Prediction

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