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Viewing 17 papers from 2019 in AI2 Israel
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    • EMNLP 2019
      Ben Bogin, Matt Gardner, Jonathan Berant
      State-of-the-art semantic parsers rely on auto-regressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zero-shot), the parser often struggles to select the correct set of database constants in the new database, due to the local…  (More)
    • EMNLP 2019
      Jonathan Herzig, Jonathan Berant
      A major hurdle on the road to conversational interfaces is the difficulty in collecting data that maps language utterances to logical forms. One prominent approach for data collection has been to automatically generate pseudo-language paired with logical forms, and paraphrase the pseudo-language to…  (More)
    • EMNLP 2019
      Hila Gonen, Yoav Goldberg
      We focus on the problem of language modeling for code-switched language, in the context of automatic speech recognition (ASR). Language modeling for code-switched language is challenging for (at least) three reasons: (1) lack of available large-scale code-switched data for training; (2) lack of a…  (More)
    • EMNLP 2019
      Matan Ben Noach, Yoav Goldberg
      Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained based on expected label proportions. We…  (More)
    • 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)