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
  • Yaara Cohen's Profile PhotoYaara CohenEngineering
  • 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
  • Alon Talmor's Profile PhotoAlon TalmorResearch
  • 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%
    • Bootstrapping Relation Extractors using Syntactic Search by Examples

      Matan Eyal, Asaf Amrami, Hillel Taub-Tabib, Yoav GoldbergEACL2021
      The advent of neural-networks in NLP brought with it substantial improvements in supervised relation extraction. However, obtaining a sufficient quantity of training data remains a key challenge. In this work we propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts. We take advantage of search engines over syntactic-graphs (Such as Shlain et al. (2020)) which expose a friendly by-example syntax. We use these to obtain positive examples by searching for sentences that are syntactically similar to user input examples. We apply this technique to relations from TACRED and DocRED and show that the resulting models are competitive with models trained on manually annotated data and on data obtained from distant supervision. The models also outperform models trained using NLG data augmentation techniques. Extending the search-based approach with the NLG method further improves the results.
    • First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT

      Benjamin Muller, Yanai Elazar, Benoît Sagot, Djamé SeddahEACL2021
      Multilingual pretrained language models have demonstrated remarkable zero-shot crosslingual transfer capabilities. Such transfer emerges by fine-tuning on a task of interest in one language and evaluating on a distinct language, not seen during the fine-tuning. Despite promising results, we still lack a proper understanding of the source of this transfer. Using a novel layer ablation technique and analyses of the model’s internal representations, we show that multilingual BERT, a popular multilingual language model, can be viewed as the stacking of two sub-networks: a multilingual encoder followed by a taskspecific language-agnostic predictor. While the encoder is crucial for cross-lingual transfer and remains mostly unchanged during finetuning, the task predictor has little importance on the transfer and can be reinitialized during fine-tuning. We present extensive experiments with three distinct tasks, seventeen typologically diverse languages and multiple domains to support our hypothesis.
    • Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI

      Alon Jacovi, Ana Marasović, Tim Miller, Yoav GoldbergFAccT2021
      Trust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely, is the nature of trust in AI? What are the prerequisites and goals of the cognitive mechanism of trust, and how can we cause these prerequisites and goals, or assess whether they are being satisfied in a given interaction? This work aims to answer these questions. We discuss a model of trust inspired by, but not identical to, sociology's interpersonal trust (i.e., trust between people). This model rests on two key properties of the vulnerability of the user and the ability to anticipate the impact of the AI model's decisions. We incorporate a formalization of 'contractual trust', such that trust between a user and an AI is trust that some implicit or explicit contract will hold, and a formalization of 'trustworthiness' (which detaches from the notion of trustworthiness in sociology), and with it concepts of 'warranted' and 'unwarranted' trust. We then present the possible causes of warranted trust as intrinsic reasoning and extrinsic behavior, and discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted. Finally, we elucidate the connection between trust and XAI using our formalization.
    • Measuring and Improving Consistency in Pretrained Language Models

      Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, Yoav GoldbergarXiv2021
      Consistency of a model — that is, the invariance of its behavior under meaning-preserving alternations in its input — is a highly desirable property in natural language processing. In this paper we study the question: Are Pretrained Language Models (PLMs) consistent with respect to factual knowledge? To this end, we create PARAREL , a high-quality resource of cloze-style query English paraphrases. It contains a total of 328 paraphrases for thirty-eight relations. Using PARAREL , we show that the consistency of all PLMs we experiment with is poor – though with high variance between relations. Our analysis of the representational spaces of PLMs suggests that they have a poor structure and are currently not suitable for representing knowledge in a robust way. Finally, we propose a method for improving model consistency and experimentally demonstrate its effectiveness

    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