AI2 ISRAEL
About
The Allen Institute for AI Israel office was founded in 2019 in Sarona, Tel Aviv. AI2's mission is to contribute to humanity through high-impact AI research and engineering.
AI2 Israel continues our mission of AI for the Common Good through groundbreaking research in natural language processing and machine learning, all in close association with the AI2 home office in Seattle, Washington.
Our Focus
The focus of AI2 Israel is bringing people closer to information, by creating and using advanced language-centered AI. As a scientific approach, we believe in combining strong linguistics-oriented foundations, state-of-the-art machine learning, and top-notch engineering, with a user oriented design.
For application domains, we focus on understanding and answering complex questions, filling in commonsense gaps in text, and enabling robust extraction of structured information from text. This is an integral part of AI2’s vision of pushing the boundaries of the algorithmic understanding of human language and advancing the common good through AI.
AI2 Israel also enjoys research relationships with top local universities Tel Aviv University and Bar-Ilan University.
Team
Yoav GoldbergResearch Director, AI2 Israel
Ron YachiniChief Operating Officer, AI2 Israel
Jonathan BerantResearch
Yaara CohenEngineering
Matan EyalResearch & Engineering
Tom HopeYoung Investigator
Yael RachmutOperations
Shoval SaddeLinguistics
Micah ShlainResearch & Engineering
Alon TalmorResearch
Hillel Taub-TabibResearch & Engineering
Reut TsarfatyResearch
Current Openings
- Software Engineer, AI2 Israel
- Research Engineer, AI2 Israel (coming soon)
Research Areas
DIY Information Extraction
Data scientists have a set of tools to work with structured data in tables. But how does one extract meaning from textual data? While NLP provides some solutions, they all require expertise in either machine learning, linguistics, or both. How do we expose advanced AI and text mining capabilities to domain experts who do not know ML or CS?
Question Understanding
The goal of this project is to develop models that understand complex questions in broad domains, and answer them from multiple information sources. Our research revolves around investigating symbolic and distributed representations that facilitate reasoning over multiple facts and offer explanations for model decisions.
Missing Elements
Current natural language processing technology aims to process what is explicitly mentioned in text. But what about the elements that are being left out of the text, yet are easily and naturally inferred by the human hearer? Can our computer programs identify and infer such elements too? In this project, we develop benchmarks and models to endow NLP applications with this capacity.
AI Gamification
The goal of this project is to involve the public in the development of better AI models. We use stimulating games alongside state-of-the-art AI models to create an appealing experience for non-scientific users. We aim to improve the ways data is collected for AI training as well as surface strengths and weaknesses of current models.
Extractive search over CORD-19 with 3 powerful query modes | AI2 Israel, DIY Information Extraction
SPIKE-CORD is powerful sentence-level, context-aware, and linguistically informed extractive search system for exploring the CORD-19 corpus.
Try the demoExtractive search over CORD-19 with 3 powerful query modes | AI2 Israel, DIY Information Extraction
SPIKE-CORD is powerful sentence-level, context-aware, and linguistically informed extractive search system for exploring the CORD-19 corpus.
Try the demo
Try the QDMR CopyNet parser | AI2 Israel, Question Understanding
Live demo of the QDMR CopyNet parser from the paper Break It Down: A Question Understanding Benchmark (TACL 2020). The parser receives a natural language question as input and returns its Question Decomposition Meaning Representation (QDMR). Each step in the decomposition constitutes a subquestion necessary to answer the original question. More info: https://allenai.github.io/Break/
Try the demoTry the QDMR CopyNet parser | AI2 Israel, Question Understanding
Live demo of the QDMR CopyNet parser from the paper Break It Down: A Question Understanding Benchmark (TACL 2020). The parser receives a natural language question as input and returns its Question Decomposition Meaning Representation (QDMR). Each step in the decomposition constitutes a subquestion necessary to answer the original question. More info: https://allenai.github.io/Break/
Try the demoRecent Papers
Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
Alon Talmor, Oyvind Tafjord, Peter Clark, Yoav Goldberg, Jonathan BerantNeurIPS • Spotlight Presentation • 2020To what extent can a neural network systematically reason over symbolic facts? Evidence suggests that large pre-trained language models (LMs) acquire some reasoning capacity, but this ability is difficult to control. Recently, it has been shown that Transformer-based models succeed in consistent… moreA Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration
Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, Moshe Koppel, Reut TsarfatyFindings of EMNLP • 2020One of the primary tasks of morphological parsers is the disambiguation of homographs. Particularly difficult are cases of unbalanced ambiguity, where one of the possible analyses is far more frequent than the others. In such cases, there may not exist sufficient examples of the minority analyses… moreImproving Compositional Generalization in Semantic Parsing
Inbar Oren, Jonathan Herzig, Nitish Gupta, Matt Gardner, Jonathan BerantFindings of EMNLP • 2020Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently. Specifically, compositional generalization, i.e., whether a model generalizes to new structures built of components observed during training, has sparked substantial interest. In this work, we… moreZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization
Tzuf Paz-Argaman, Y. Atzmon, Gal Chechik, Reut TsarfatyFindings of EMNLP • 2020We study the problem of recognizing visual entities from the textual descriptions of their classes. Specifically, given birds' images with free-text descriptions of their species, we learn to classify images of previously-unseen species based on specie descriptions. This setup has been studied in… moreA Simple and Effective Model for Answering Multi-span Questions
Elad Segal, Avia Efrat, Mor Shoham, Amir Globerson, Jonathan BerantEMNLP • 2020Models for reading comprehension (RC) commonly restrict their output space to the set of all single contiguous spans from the input, in order to alleviate the learning problem and avoid the need for a model that generates text explicitly. However, forcing an answer to be a single span can be… more
Recent Press
View All AI2 Israel Pressמערכת בינה מלאכותית עברה בהצטיינות יתרה מבחן במדעים של כיתה ח' (Artificial Intelligence System Cum Laude Passed 8th Grade Science Test)
Haaretz
September 6, 2019
September 6, 2019
המחיר המושתק של בינה מלאכותית (The secret price of artificial intelligence)
ynet
August 12, 2019
August 12, 2019
Allen Institute for Artificial Intelligence to Open Israeli Branch
CTech
May 20, 2019
May 20, 2019
“Please join us to tackle an extraordinary set of scientific and engineering challenges. Let’s make history together.”
— Oren Etzioni