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 About

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 Goldberg's Profile PhotoYoav GoldbergResearch Director, AI2 Israel
  • Ron Yachini's Profile PhotoRon YachiniChief Operating Officer, AI2 Israel
  • Jonathan Berant's Profile PhotoJonathan BerantResearch
  • Matan Eyal's Profile PhotoMatan EyalResearch & Engineering
  • Tom Hope's Profile PhotoTom HopeYoung Investigator
  • Yael Rachmut's Profile PhotoYael RachmutOperations
  • Shoval Sadde's Profile PhotoShoval SaddeLinguistics
  • Micah Shlain's Profile PhotoMicah ShlainResearch & Engineering
  • Hillel  Taub-Tabib's Profile PhotoHillel Taub-TabibResearch & Engineering
  • Reut Tsarfaty's Profile PhotoReut TsarfatyResearch

Current Openings

AI2 Israel is a non-profit offering exceptional opportunities for researchers and engineers to develop AI for the common good. We are currently looking for outstanding software engineers and research engineers. Candidates should send their CV to: ai2israel-cv@allenai.org

AI2 Israel Office

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 demo
    SPIKE-CORD Demo Image
  • SPIKE-CORD Demo Image
    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 demo
  • Break QDMR representation
    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 demo
  • Break QDMR representation
    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 demo
    • Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

      Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, Jonathan BerantTACL2021
      A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly. In this work, we introduce STRATEGYQA, a question answering (QA) benchmark where the required reasoning steps are implicit in the question, and should be inferred using a strategy. A fundamental challenge in this setup is how to elicit such creative questions from crowdsourcing workers, while covering a broad range of potential strategies. We propose a data collection procedure that combines term-based priming to inspire annotators, careful control over the annotator population, and adversarial filtering for eliminating reasoning shortcuts. Moreover, we annotate each question with (1) a decomposition into reasoning steps for answering it, and (2) Wikipedia paragraphs that contain the answers to each step. Overall, STRATEGYQA includes 2,780 examples, each consisting of a strategy question, its decomposition, and evidence paragraphs. Analysis shows that questions in STRATEGYQA are short, topicdiverse, and cover a wide range of strategies. Empirically, we show that humans perform well (87%) on this task, while our best baseline reaches an accuracy of ∼ 66%
    • Few-Shot Question Answering by Pretraining Span Selection

      Ori Ram, Yuval Kirstain, Jonathan Berant, A. Globerson, Omer LevyACL2021
      In a number of question answering (QA) benchmarks, pretrained models have reached human parity through fine-tuning on an order of 100,000 annotated questions and answers. We explore the more realistic few-shot setting, where only a few hundred training examples are available. We show that standard span selection models perform poorly, highlighting the fact that current pretraining objective are far removed from question answering. To address this, we propose a new pretraining scheme that is more suitable for extractive question answering. Given a passage with multiple sets of recurring spans, we mask in each set all recurring spans but one, and ask the model to select the correct span in the passage for each masked span. Masked spans are replaced with a special token, viewed as a question representation, that is later used during fine-tuning to select the answer span. The resulting model obtains surprisingly good results on multiple benchmarks, e.g., 72.7 F1 with only 128 examples on SQuAD, while maintaining competitive (and sometimes better) performance in the high-resource setting. Our findings indicate that careful design of pretraining schemes and model architecture can have a dramatic effect on performance in the few-shot settings.
    • Neural Extractive Search

      Shaul Ravfogel, Hillel Taub-Tabib, Yoav GoldbergACL • Demo Track2021
      Domain experts often need to extract structured information from large corpora. We advocate for a search paradigm called “extractive search”, in which a search query is enriched with capture-slots, to allow for such rapid extraction. Such an extractive search system can be built around syntactic structures, resulting in high-precision, low-recall results. We show how the recall can be improved using neural retrieval and alignment. The goals of this paper are to concisely introduce the extractive-search paradigm; and to demonstrate a prototype neural retrieval system for extractive search and its benefits and potential. Our prototype is available at https://spike. neural-sim.apps.allenai.org/ and a video demonstration is available at https:// vimeo.com/559586687.
    • Question Decomposition with Dependency Graphs

      Matan Hasson, Jonathan BerantACL-IJCNLP • 15th Workshop on Structured Prediction for NLP2021
      QDMR is a meaning representation for complex questions, which decomposes questions into a sequence of atomic steps. While stateof-the-art QDMR parsers use the common sequence-to-sequence (seq2seq) approach, a QDMR structure fundamentally describes labeled relations between spans in the input question, and thus dependency-based approaches seem appropriate for this task. In this work, we present a QDMR parser that is based on dependency graphs (DGs), where nodes in the graph are words and edges describe logical relations that correspond to the different computation steps. We propose (a) a nonautoregressive graph parser, where all graph edges are computed simultaneously, and (b) a seq2seq parser that uses gold graph as auxiliary supervision. We find that a graph parser leads to a moderate reduction in performance (0.47→0.44), but to a 16x speed-up in inference time due to the non-autoregressive nature of the parser, and to improved sample complexity compared to a seq2seq model. Second, a seq2seq model trained with auxiliary graph supervision has better generalization to new domains compared to a seq2seq model, and also performs better on questions with long sequences of computation steps.
    • Provable Limitations of Acquiring Meaning from Ungrounded Form: What will Future Language Models Understand?

      William Merrill, Yoav Goldberg, Roy Schwartz, Noah A. SmithTACL2021
      Language models trained on billions of tokens have recently led to unprecedented results on many NLP tasks. This success raises the question of whether, in principle, a system can ever “understand” raw text without access to some form of grounding. We formally investigate the abilities of ungrounded systems to acquire meaning. Our analysis focuses on the role of “assertions”: contexts within raw text that provide indirect clues about underlying semantics. We study whether assertions enable a system to emulate representations preserving semantic relations like equivalence. We find that assertions enable semantic emulation if all expressions in the language are referentially transparent. However, if the language uses non-transparent patterns like variable binding, we show that emulation can become an uncomputable problem. Finally, we discuss differences between our formal model and natural language, exploring how our results generalize to a modal setting and other semantic relations. Together, our results suggest that assertions in code or language do not provide sufficient signal to fully emulate semantic representations. We formalize ways in which ungrounded language models appear to be fundamentally limited in their ability to “understand”.

    מערכת בינה מלאכותית עברה בהצטיינות יתרה מבחן במדעים של כיתה ח' (Artificial Intelligence System Cum Laude Passed 8th Grade Science Test)

    Haaretz
    September 6, 2019
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    המחיר המושתק של בינה מלאכותית (The secret price of artificial intelligence)

    ynet
    August 12, 2019
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    Allen Institute for Artificial Intelligence to Open Israeli Branch

    CTech
    May 20, 2019
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    “Please join us to tackle an extraordinary set of scientific and engineering challenges. Let’s make history together.”
    Oren Etzioni