Engineering Playground
Artificial Intelligence School
AI School is a mobile app designed to teach the basics of artificial intelligence, machine learning, and deep learning. Includes a comprehensive lesson plan for learning fundamental principles.
Mihail Eric
Available on both the App Store and Google Play!
Key Value Retrieval Networks for Task-Oriented Dialogue
We demonstrate the efficacy of a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discourse through a novel key-value retrieval mechanism.
Mihail Eric, Lakshmi Krishnan, Francois Charette, Christopher D. Manning
SIGDial 2017 Oral Presentation, arXiv:1705.05414
The Pragmatics of Indirect Commands in Collaborative Discourse
We show that models with domain-specific grounding can effectively realize the pragmatic reasoning that is necessary for more robust natural language interaction.
Matthew Lamm* and Mihail Eric*
International Conference on Computational Semantics 2017, arXiv:1705.03454
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
To model both structured knowledge and unstructured language in a novel dialogue setting, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
He He, Anusha Balakrishnan, Mihail Eric, Percy Liang
ACL 2017, arXiv:1704.07130
A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue
We show the effectiveness of simple sequence-to-sequence neural architectures with a copy mechanism, outperforming more sophisticated models on a standard task-oriented dialogue dataset.
Mihail Eric, Christopher D. Manning
EACL 2017 Oral Presentation, arXiv:1701.04024
SceneSeer: 3D Scene Design with Natural Language
We present SceneSeer: an interactive text to 3D scene generation system with a learned spatial knowledge base that allows a user to design 3D scenes using natural language.
Angel X. Chang, Mihail Eric, Manolis Savva, Christopher D. Manning
Preprint 2017, arXiv:1703.00050
Technical Reports
Using Contextual Information for Neural Natural Language Inference
We investigate neural memory network architectures for the task of natural language inference and propose models for using attention across relevant semantic phrases to inform common sense reasoning.
Chris Billovits* and Mihail Eric*
Preprint 2016
Hitting Depth: Investigating Robustness to Adversarial Examples in Deep Convolutional Neural Networks
We show a process for visualizing and identifying changes in activations between adversarial images and their regular counterparts and propose a Bayesian framework for improving prediction accuracy on adversarial examples.
Chris Billovits* and Mihail Eric* and Nipun Agrawal*
Preprint 2016
Wordwise Inference and Entailment Now
We implement a random forest classifier with a carefully engineered and selected collection of linguistic and semantic features for the task of natural language inference, achieving an F1 of 80.9% on the SemEval-2014 Dataset.
Chris Billovits* and Mihail Eric* and Chris Guthrie*
Preprint 2016