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data-reactid="37"><a href="https://scholar.google.com/citations?user=4kJYLpcAAAAJ&hl=en" data-reactid="38"><i class="icon-graduation-cap" data-reactid="39"></i></a></li></ul></div><div class="twitterfeed" data-reactid="40"><a class="twitter-timeline" data-width="400" data-height="400" data-theme="light" data-link-color="#2B7BB9" href="https://twitter.com/mihail_eric?ref_src=twsrc%5Etfw" data-reactid="41">
16Tweets by mihail_eric</a></div></div></div></div><div class="content" data-reactid="42"><div class="content__inner" data-reactid="43"><div data-reactid="44"><div class="section-title" data-reactid="45">Engineering Playground</div><div class="container" data-reactid="46"><div class="image" data-reactid="47"><a href="https://rickandmortify.com/" data-reactid="48"><img src="/static/randm.f6f39e65.png" alt="Rick and Mortify" data-reactid="49"/></a></div><div class="project-details" data-reactid="50"><div class="title" data-reactid="51"><a href="https://rickandmortify.com/" data-reactid="52">Rick and Mortify</a></div><div class="description" data-reactid="53">When will we see an AI system listed in the closing credits of your favorite feature-length film? Rick and Mortify is a tool we built using the state-of-the-art in large vision and language models to create never-before-seen episodes of Rick and Morty with minimal human intervention.</div></div></div><div class="container" data-reactid="54"><div class="image" data-reactid="55"><a href="https://autoplot.app" data-reactid="56"><img src="/static/autoplot_ex.7f8aa28c.jpeg" alt="AutoPlot" data-reactid="57"/></a></div><div class="project-details" data-reactid="58"><div class="title" data-reactid="59"><a href="https://autoplot.app" data-reactid="60">AutoPlot</a></div><div class="description" data-reactid="61">What if you could do any data analysis by just speaking in natural language? AutoPlot is a tool we developed to explore the limits of next generation human interfaces for visually understanding data.</div></div></div><div class="container" data-reactid="62"><div class="image" data-reactid="63"><a href="https://www.confetti.ai/" data-reactid="64"><img src="/static/logo_white_letter_vertical_512.344ee9dc.png" alt="Confetti AI" data-reactid="65"/></a></div><div class="project-details" data-reactid="66"><div class="title" data-reactid="67"><a href="https://www.confetti.ai/" data-reactid="68">Confetti AI</a></div><div class="description" data-reactid="69">Confetti AI is an educational platform helping people learn the skills to succeed in artificial intelligence careers. It provides a collection of targeted resources and tools to empower the next generation of AI practitioners. We were acquired by Towards AI in 2022.</div></div></div><div class="container" data-reactid="70"><div class="image" data-reactid="71"><a href="https://wheredoestaxmoneygo.mihaileric.com/" data-reactid="72"><img src="/static/wheredoestaxmoneygo.732e57da.png" alt="Where Does Tax Money Go" data-reactid="73"/></a></div><div class="project-details" data-reactid="74"><div class="title" data-reactid="75"><a href="https://wheredoestaxmoneygo.mihaileric.com/" data-reactid="76">Where Does Tax Money Go</a></div><div class="description" data-reactid="77">Every year, your hard-earned money gets shuffled to taxes. The purpose of this little project was an attempt to answer the simple question: what does your tax money actually pay for?</div></div></div><div class="container-edge" data-reactid="78"><div class="image" data-reactid="79"><a href="https://itunes.apple.com/us/app/artificial-intelligence-school/id1369987569?mt=8" data-reactid="80"><img src="/static/aiSchoolIcon.5c8f1bcd.png" alt="Artificial Intelligence School" data-reactid="81"/></a></div><div class="project-details" data-reactid="82"><div class="title" data-reactid="83"><a href="https://itunes.apple.com/us/app/artificial-intelligence-school/id1369987569?mt=8" data-reactid="84">Artificial Intelligence School</a></div><div class="description" data-reactid="85">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.</div><div class="meta" data-reactid="86">Available on both the App Store and Google Play!</div></div></div><div class="section-title" data-reactid="87">Publications</div><div class="container" data-reactid="88"><div class="image" data-reactid="89"><a href="https://www.confetti.ai/assets/ml-primer/ml_primer.pdf" data-reactid="90"><img src="/static/ml_primer_img.1cec936b.png" alt="A Machine Learning Primer" data-reactid="91"/></a></div><div class="project-details" data-reactid="92"><div class="title" data-reactid="93"><a href="https://www.confetti.ai/assets/ml-primer/ml_primer.pdf" data-reactid="94">A Machine Learning Primer</a></div><div class="description" data-reactid="95">I provide an overview of key machine learning concepts that show up in data science and ML interviews. This primer includes core theory as well as practice problems to test your knowledge.</div><div class="authors" data-reactid="96">Mihail Eric</div><div class="meta" data-reactid="97">Confetti AI</div></div></div><div class="container" data-reactid="98"><div class="image" data-reactid="99"><a href="https://arxiv.org/abs/2010.08684" data-reactid="100"><img src="/static/observers.6e0cc683.png" alt="Example-Driven Intent Prediction With Observers" data-reactid="101"/></a></div><div class="project-details" data-reactid="102"><div class="title" data-reactid="103"><a href="https://arxiv.org/abs/2010.08684" data-reactid="104">Example-Driven Intent Prediction With Observers</a></div><div class="description" data-reactid="105">We propose two approaches for improving the generalizability of utterance classification models (example-driven training and observers) that when used in combination achieve state-of-the-art results in full-data and few-shot settings on several intent prediction datasets.</div><div class="authors" data-reactid="106">Shikib Mehri, Mihail Eric, Dilek Hakkani-Tur</div>
16<div class="meta" data-reactid="107">arXiv:2010.08684</div></div></div><div class="container" data-reactid="108"><div class="image" data-reactid="109"><a href="https://arxiv.org/abs/2009.13570" data-reactid="110"><img src="/static/glue2.57b4ac84.jpg" alt="DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue" data-reactid="111"/></a></div><div class="project-details" data-reactid="112"><div class="title" data-reactid="113"><a href="https://arxiv.org/abs/2009.13570" data-reactid="114">DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue</a></div><div class="description" data-reactid="115">We introduce DialoGLUE (Dialogue Language Understanding Evaluation), a public benchmark consisting of 7 task-oriented dialogue datasets covering 4 distinct natural language understanding tasks. We introduce new models that achieve state-of-the-art results on 5/7 datasets in the benchmark.</div><div class="authors" data-reactid="116">Shikib Mehri, Mihail Eric, Dilek Hakkani-Tur</div><div class="meta" data-reactid="117">arXiv:2009.1357</div></div></div><div class="container" data-reactid="118"><div class="image" data-reactid="119"><a href="https://www.aclweb.org/anthology/2020.nlp4convai-1.0.pdf" data-reactid="120"><img src="/static/aclworkshop.50a82e6e.png" alt="Proceedings of 2nd ACL NLP for Conversational AI Workshop" data-reactid="121"/></a></div><div class="project-details" data-reactid="122"><div class="title" data-reactid="123"><a href="https://www.aclweb.org/anthology/2020.nlp4convai-1.0.pdf" data-reactid="124">Proceedings of 2nd ACL NLP for Conversational AI Workshop</a></div><div class="description" data-reactid="125">The goal of this workshop is to bring together NLP researchers and practitioners in different fields, alongside experts in speech and machine learning, to discuss the current state-of-the-art and new approaches, to share insights and challenges, to bridge the gap between academic research and realworld product deployment, and to shed the light on future directions.</div><div class="authors" data-reactid="126">Tsung-Hsien Wen, Asli Celikyilmaz, Zhou Yu, Alexandros Papangelis, Mihail Eric, Anuj Kumar, Inigo Casanueva, Rushin Shah</div><div class="meta" data-reactid="127">ACL 2020</div></div></div><div class="container" data-reactid="128"><div class="image" data-reactid="129"><a href="https://arxiv.org/abs/2006.03533" data-reactid="130"><img src="/static/kmdm.2685f142.png" alt="Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access" data-reactid="131"/></a></div><div class="project-details" data-reactid="132"><div class="title" data-reactid="133"><a href="https://arxiv.org/abs/2006.03533" data-reactid="134">Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access</a></div><div class="description" data-reactid="135">We propose to expand coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources.</div><div class="authors" data-reactid="136">Seokhwan Kim, Mihail Eric, Karthik Gopalakrishnan, Behnam Hedayatnia, Yang Liu, Dilek Hakkani-Tur</div><div class="meta" data-reactid="137">SIGDial 2020, arXiv:2006.03533</div></div></div><div class="container" data-reactid="138"><div class="image" data-reactid="139"><a href="https://arxiv.org/abs/2005.12529" data-reactid="140"><img src="/static/policy.27378725.png" alt="Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems" data-reactid="141"/></a></div><div class="project-details" data-reactid="142"><div class="title" data-reactid="143"><a href="https://arxiv.org/abs/2005.12529" data-reactid="144">Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems</a></div><div class="description" data-reactid="145">We propose a technique for using a dialogue policy to plan the content and style of target responses in the form of an action plan.</div><div class="authors" data-reactid="146">Behnam Hedayatnia, Karthik Gopalakrishnan, Seokhwan Kim, Yang Liu, Mihail Eric, Dilek Hakkani-Tur</div><div class="meta" data-reactid="147">Preprint 2020, arXiv:2005.12529</div></div></div><div class="container" data-reactid="148"><div class="image" data-reactid="149"><a href="https://arxiv.org/abs/1912.00915" data-reactid="150"><img src="/static/vdn.0b2217c4.png" alt="Just Ask: An Interactive Learning Framework for Vision and Language Navigation" data-reactid="151"/></a></div><div class="project-details" data-reactid="152"><div class="title" data-reactid="153"><a href="https://arxiv.org/abs/1912.00915" data-reactid="154">Just Ask: An Interactive Learning Framework for Vision and Language Navigation</a></div><div class="description" data-reactid="155">We propose a novel scheme for interactive human-in-the-loop learning, achieving more data-efficient performance on a vision and language task.</div><div class="authors" data-reactid="156">Ta-Chung Chi, Mihail Eric, Seokhwan Kim, Minmin Shen, Dilek Hakkani-Tur</div><div class="meta" data-reactid="157">AAAI 2020, arXiv:1912.00915</div></div></div><div class="container" data-reactid="158"><div class="image" data-reactid="159"><a href="https://arxiv.org/abs/1907.01669" data-reactid="160"><img 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Manning</div><div class="meta" data-reactid="177">SIGDial 2017 Oral Presentation, arXiv:1705.05414</div></div></div><div class="container" data-reactid="178"><div class="image" data-reactid="179"><a href="https://arxiv.org/abs/1705.03454" data-reactid="180"><img src="/static/cards.a2dcd547.png" alt="The Pragmatics of Indirect Commands in Collaborative Discourse" data-reactid="181"/></a></div><div class="project-details" data-reactid="182"><div class="title" data-reactid="183"><a href="https://arxiv.org/abs/1705.03454" data-reactid="184">The Pragmatics of Indirect Commands in Collaborative Discourse</a></div><div class="description" data-reactid="185">We show that models with domain-specific grounding can effectively realize the pragmatic reasoning that is necessary for more robust natural language interaction.</div><div class="authors" data-reactid="186">Matthew Lamm* and Mihail Eric*</div><div class="meta" data-reactid="187">International Conference on Computational Semantics 2017, arXiv:1705.03454</div></div></div><div class="container" data-reactid="188"><div class="image" data-reactid="189"><a href="https://arxiv.org/abs/1704.07130" data-reactid="190"><img src="/static/mutual.89e44312.png" alt="Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings" data-reactid="191"/></a></div><div class="project-details" data-reactid="192"><div class="title" data-reactid="193"><a href="https://arxiv.org/abs/1704.07130" data-reactid="194">Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings</a></div><div class="description" data-reactid="195">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.</
16div><div class="authors" data-reactid="196">He He, Anusha Balakrishnan, Mihail Eric, Percy Liang</div><div class="meta" data-reactid="197">ACL 2017, arXiv:1704.07130</div></div></div><div class="container" data-reactid="198"><div class="image" data-reactid="199"><a href="https://arxiv.org/abs/1701.04024" data-reactid="200"><img src="/static/copy.e6672550.png" alt="A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue" data-reactid="201"/></a></div><div class="project-details" data-reactid="202"><div class="title" data-reactid="203"><a href="https://arxiv.org/abs/1701.04024" data-reactid="204">A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue</a></div><div class="description" data-reactid="205">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.</div><div class="authors" data-reactid="206">Mihail Eric, Christopher D. Manning</div><div class="meta" data-reactid="207">EACL 2017 Oral Presentation, arXiv:1701.04024</div></div></div><div class="container-edge" data-reactid="208"><div class="image" data-reactid="209"><a href="https://arxiv.org/abs/1703.00050" data-reactid="210"><img src="/static/sceneseer.7fe00f71.png" alt="SceneSeer: 3D Scene Design with Natural Language" data-reactid="211"/></a></div><div class="project-details" data-reactid="212"><div class="title" data-reactid="213"><a href="https://arxiv.org/abs/1703.00050" data-reactid="214">SceneSeer: 3D Scene Design with Natural Language</a></div><div class="description" data-reactid="215">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.</div><div class="authors" data-reactid="216">Angel X. Chang, Mihail Eric, Manolis Savva, Christopher D. Manning</div><div class="meta" data-reactid="217">Preprint 2017, arXiv:1703.00050</div></div></div><div class="section-title" data-reactid="218">Technical Reports</div><div class="container" data-reactid="219"><div class="image" data-reactid="220"><a href="https://cs224d.stanford.edu/reports/billovits.pdf" data-reactid="221"><img src="/static/nnli.b817db33.png" alt="Using Contextual Information for Neural Natural Language Inference" data-reactid="222"/></a></div><div class="project-details" data-reactid="223"><div class="title" data-reactid="224"><a href="https://cs224d.stanford.edu/reports/billovits.pdf" data-reactid="225">Using Contextual Information for Neural Natural Language Inference</a></div><div class="description" data-reactid="226">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.</div><div class="authors" data-reactid="227">Chris Billovits* and Mihail Eric*</div><div class="meta" data-reactid="228">Preprint 2016</div></div></div><div class="container" data-reactid="229"><div class="image" data-reactid="230"><a href="http://cs231n.stanford.edu/reports/2016/pdfs/119_Report.pdf" data-reactid="231"><img src="/static/adversarial.f23dd8e4.png" alt="Hitting Depth: Investigating Robustness to Adversarial Examples in Deep Convolutional Neural Networks" data-reactid="232"/></a></div><div class="project-details" data-reactid="233"><div class="title" data-reactid="234"><a href="http://cs231n.stanford.edu/reports/2016/pdfs/119_Report.pdf" data-reactid="235">Hitting Depth: Investigating Robustness to Adversarial Examples in Deep Convolutional Neural Networks</a></div><div class="description" data-reactid="236">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.</div><div class="authors" data-reactid="237">Chris Billovits* and Mihail Eric* and Nipun Agrawal*</div><div class="meta" data-reactid="238">Preprint 2016</div></div></div><div class="container-edge" data-reactid="239"><div class="image" data-reactid="240"><a href="https://www.semanticscholar.org/paper/WIEN-%3A-Wordwise-Inference-and-Entailment-Now-Or-%3A-Billovits-Eric/b525f726265aca03d07021b0e9dec14c6ace0890" data-reactid="241"><img src="/static/random_forest.c98af35b.png" alt="Wordwise Inference and Entailment Now" data-reactid="242"/></a></div><div class="project-details" data-reactid="243"><div class="title" data-reactid="244"><a href="https://www.semanticscholar.org/paper/WIEN-%3A-Wordwise-Inference-and-Entailment-Now-Or-%3A-Billovits-Eric/b525f726265aca03d07021b0e9dec14c6ace0890" data-reactid="245">Wordwise Inference and Entailment Now</a></div><div class="description" data-reactid="246">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.</div><div class="authors" data-reactid="247">Chris Billovits* and Mihail Eric* and Chris Guthrie*</div><div class="meta" data-reactid="248">Preprint 2016</div></div></div></div></div></div></div></div></div>
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