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
Smadar CohenOperations
Matan EyalResearch & Engineering
Tom HopeResearch
Menny PinhasovEngineering
Micah ShlainResearch & Engineering
Hillel Taub-TabibResearch & Engineering
Aryeh TiktinskyResearch & Engineering
Reut TsarfatyResearch
Current Openings
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?
Learn more about DIY Information ExtractionQuestion 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.
Learn more about Question UnderstandingMissing 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.
Learn more about Missing ElementsAI 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.
Learn more about AI GamificationLive 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…
Try the demo
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…
Try the demo
CrowdSense is an interactive effort to better understand what types of questions people consider to be common sense.
Try the demoCrowdSense is an interactive effort to better understand what types of questions people consider to be common sense.
Try the demoRecent Papers
Linear Adversarial Concept Erasure
Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan CotterellICML • 2022 We formulate the problem of identifying and erasing a linear subspace that corresponds to a given concept, in order to prevent linear predictors from recovering the concept. We model this problem as a constrained, linear minimax game, and show that existing…A Dataset for N-ary Relation Extraction of Drug Combinations
Aryeh Tiktinsky, Vijay Viswanathan, Danna Niezni, Dana Azagury, Yosi Shamay, Hillel Taub-Tabib, Tom Hope, Yoav GoldbergNAACL • 2022 Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available…Weakly Supervised Text-to-SQL Parsing through Question Decomposition
Tomer Wolfson, Daniel Deutch, Jonathan BerantFindings of NAACL • 2022 Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expertise in annotating natural language (NL) utterances with corresponding SQL queries. In this work, we…Draw Me a Flower: Grounding Formal Abstract Structures Stated in Informal Natural Language
Royi Lachmy, Valentina Pyatkin, Reut TsarfatyACL • 2022 Forming and interpreting abstraction is a core process in human communication. In particular, when giving and performing complex instructions stated in natural language (NL), people may naturally evoke abstract constructs such as objects, loops, conditions…Large Scale Substitution-based Word Sense Induction
Matan Eyal, Shoval Sadde, Hillel Taub-Tabib, Yoav GoldbergACL • 2022 We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory and where…
Recent Press
Artificial-intelligence tools aim to tame the coronavirus literature
June 9, 2020
מערכת בינה מלאכותית עברה בהצטיינות יתרה מבחן במדעים של כיתה ח' (Artificial Intelligence System Cum Laude Passed 8th Grade Science Test)
September 6, 2019
המחיר המושתק של בינה מלאכותית (The secret price of artificial intelligence)
August 12, 2019
Allen Institute for Artificial Intelligence to Open Israeli Branch
May 20, 2019