no code implementations • ACL 2022 • Anton Belyy, Chieh-Yang Huang, Jacob Andreas, Emmanouil Antonios Platanios, Sam Thomson, Richard Shin, Subhro Roy, Aleksandr Nisnevich, Charles Chen, Benjamin Van Durme
Collecting data for conversational semantic parsing is a time-consuming and demanding process.
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).
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.
1 code implementation • 24 May 2022 • Elias Stengel-Eskin, Emmanouil Antonios Platanios, Adam Pauls, Sam Thomson, Hao Fang, Benjamin Van Durme, Jason Eisner, Yu Su
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.
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.
no code implementations • 8 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.
no code implementations • NAACL 2021 • Pengcheng Yin, Hao Fang, Graham Neubig, Adam Pauls, Emmanouil Antonios Platanios, Yu Su, Sam Thomson, Jacob Andreas
We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers.
1 code implementation • EMNLP 2021 • Richard Shin, Christopher H. Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme
We explore the use of large pretrained language models as few-shot semantic parsers.
1 code implementation • 16 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.
no code implementations • 17 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.
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.
1 code implementation • 9 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.
no code implementations • 7 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.
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.
no code implementations • 25 Sep 2019 • Emmanouil Antonios Platanios, Maruan Al-Shedivat, Eric Xing, Tom Mitchell
Many machine learning systems today are trained on large amounts of human-annotated data.
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.
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.
no code implementations • 4 Jun 2018 • Emmanouil Antonios Platanios
Model selection is a problem that has occupied machine learning researchers for a long time.
no code implementations • 4 Jun 2018 • Emmanouil Antonios Platanios, Alex Smola
We propose an algorithm for deep learning on networks and graphs.
1 code implementation • 26 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.