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Viewing 23 papers in AI2 Israel
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    • arXiv 2019
      Mor Geva, Yoav Goldberg, Jonathan Berant
      Crowdsourcing has been the prevalent paradigm for creating natural language understanding datasets in recent years. A common crowdsourcing practice is to recruit a small number of high-quality workers, and have them massively generate examples. Having only a few workers generate the majority of…  (More)
    • ACL 2019
      Alon Talmor, Jonathan Berant
      A large number of reading comprehension (RC) datasets has been created recently, but little analysis has been done on whether they generalize to one another, and the extent to which existing datasets can be leveraged for improving performance on new ones. In this paper, we conduct such an…  (More)
    • ACL 2019
      Ben Bogin, Jonathan Berant, Matt Gardner
      Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In SPIDER, a recently-released text-to-SQL dataset, new and complex DBs are given at test time, and so…  (More)
    • NAACL 2019
      Hila Gonen, Yoav Goldberg
      Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious concern. Several recent works tackle…  (More)
    • Award Best Resource Paper
      NAACL 2019
      Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan Berant
      When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background. To investigate question answering with…  (More)
    • NAACL 2019
      Amit Mor-Yosef, Ido Dagan, Yoav Goldberg
      Data-to-text generation can be conceptually divided into two parts: ordering and structuring the information (planning), and generating fluent language describing the information (realization). Modern neural generation systems conflate these two steps into a single end-to-end differentiable system…  (More)
    • NAACL 2019
      Noa Yehezkel, Jacob Goldberger, Yoav Goldberg
      The problem of learning to translate between two vector spaces given a set of aligned points arises in several application areas of NLP. Current solutions assume that the lexicon which defines the alignment pairs is noise-free. We consider the case where the set of aligned points is allowed to…  (More)
    • NAACL 2019
      Or Gorodissky, Yoav Chai, Yotam Gil, Jonathan Berant
      We show that a neural network can learn to imitate the optimization process performed by white-box attack in a much more efficient manner. We train a black-box attack through this imitation process and show our attack is 19x-39x faster than the white-box attack and also that we can perform a black…  (More)
    • NAACL 2019
      Mor Geva, Eric Malmi, Idan Szpektor, Jonathan Berant
      Sentence fusion is the task of joining several independent sentences into a single coherent text. Current datasets for sentence fusion are small and insufficient for training modern neural models. In this paper, we propose a method for automatically-generating fusion examples from raw text and…  (More)
    • NAACL 2019
      Guy Tevet, Gavriel Habib, Vered Shwartz, Jonathan Berant
      Generative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of “exposure bias”. However, A major hurdle for understanding the potential of GANs for text generation is the lack of a clear…  (More)
    • NAACL 2019
      Dor Muhlgay, Jonathan Herzig, Jonathan Berant
      Training models to map natural language instructions to programs given target world supervision only requires searching for good programs at training time. Search is commonly done using beam search in the space of partial programs or program trees, but as the length of the instructions grows…  (More)
    • NAACL 2019
      Shauli Ravfogel, Yoav Goldberg, Tal Linzen
      How do typological properties such as word order and morphological case marking affect the ability of neural sequence models to acquire the syntax of a language? Cross-linguistic comparisons of RNNs' syntactic performance (e.g., on subject-verb agreement prediction) are complicated by the fact that…  (More)
    • ICLR 2019
      Alon Jacovi, Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kour, Jonathan Berant
      Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of existing functions that programmatically solve different tasks in a precise manner eliminating the need for training. In…  (More)
    • NeurIPS 2018
      Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson
      Machine understanding of complex images is a key goal of artificial intelligence. One challenge underlying this task is that visual scenes contain multiple inter-related objects, and that global context plays an important role in interpreting the scene. A natural modeling framework for capturing…  (More)
    • NeurIPS 2018
      Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc Le, Ni Lao
      This paper presents Memory Augmented Policy Optimization (MAPO): a novel policy optimization formulation that incorporates a memory buffer of promising trajectories to reduce the variance of policy gradient estimates for deterministic environments with discrete actions. The formulation expresses…  (More)
    • EMNLP • Workshop: Analyzing and interpreting neural networks for NLP 2018
      Alon Jacovi, Oren Sar Shalom, Yoav Goldberg
      We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters into image space, but for discrete sequence inputs CNNs remain a mystery. We aim to understand the method by which the…  (More)
    • EMNLP 2018
      Jonathan Herzig, Jonathan Berant
      Building a semantic parser quickly in a new domain is a fundamental challenge for conversational interfaces, as current semantic parsers require expensive supervision and lack the ability to generalize to new domains. In this paper, we introduce a zero-shot approach to semantic parsing that can…  (More)
    • EMNLP • Workshop: Analyzing and interpreting neural networks for NLP 2018
      Shauli Ravfogel, Francis M. Tyers, Yoav Goldberg
      Sequential neural networks models are powerful tools in a variety of Natural Language Processing (NLP) tasks. The sequential nature of these models raises the questions: to what extent can these models implicitly learn hierarchical structures typical to human language, and what kind of grammatical…  (More)
    • EMNLP 2018
      Yanai Elazar, Yoav Goldberg
      Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in—and can be recovered from—the intermediate representations learned by text-based neural…  (More)
    • EMNLP 2018
      Asaf Amrami, Yoav Goldberg
      An established method for Word Sense Induction (WSI) uses a language model to predict probable substitutes for target words, and induces senses by clustering these resulting substitute vectors. We replace the ngram-based language model (LM) with a recurrent one. Beyond being more accurate, the use…  (More)
    • ACL • Workshop on Semantic Parsing 2014
      Xuchen Yao, Jonathan Berant, and Benjamin Van Durme
      We contrast two seemingly distinct approaches to the task of question answering (QA) using Freebase: one based on information extraction techniques, the other on semantic parsing. Results over the same test-set were collected from two state-ofthe-art, open-source systems, then analyzed in…  (More)
    • Award Best Paper Award
      EMNLP 2014
      Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Brad Huang, Christopher D. Manning, Abby Vander Linden, Brittany Harding, and Peter Clark
      Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this paper, we focus on a new reading comprehension task that requires complex reasoning over a single document. The input is a paragraph…  (More)
    • EMNLP 2013
      Aju Thalappillil Scaria, Jonathan Berant, Mengqiu Wang, Christopher D. Manning, Justin Lewis, Brittany Harding, and Peter Clark
      Biological processes are complex phenomena involving a series of events that are related to one another through various relationships. Systems that can understand and reason over biological processes would dramatically improve the performance of semantic applications involving inference such as…  (More)