Publications (112)
Inducing Regular Grammars Using Recurrent Neural Networks
Mor Cohen, Avi Caciularu, Idan Rejwan +1
Grammar induction is the task of learning a grammar from a set of examples. Recently, neural networks have been shown to be powerful learning machines that can identify patterns in…
Text Segmentation as a Supervised Learning Task
Omri Koshorek, Adir Cohen, Noam Mor +2
Text segmentation, the task of dividing a document into contiguous segments based on its semantic structure, is a longstanding challenge in language understanding. Previous work on…
Span-based Semantic Parsing for Compositional Generalization
Jonathan Herzig, Jonathan Berant
Despite the success of sequence-to-sequence (seq2seq) models in semantic parsing, recent work has shown that they fail in compositional generalization, i.e., the ability to general…
Simplifying and Understanding State Space Models with Diagonal Linear RNNs
Ankit Gupta, Harsh Mehta, Jonathan Berant
Sequence models based on linear state spaces (SSMs) have recently emerged as a promising choice of architecture for modeling long range dependencies across various modalities. Howe…
Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization
Inbar Oren, Jonathan Herzig, Jonathan Berant
Modern semantic parsers suffer from two principal limitations. First, training requires expensive collection of utterance-program pairs. Second, semantic parsers fail to generalize…
A Simple and Effective Model for Answering Multi-span Questions
Elad Segal, Avia Efrat, Mor Shoham +2
Models for reading comprehension (RC) commonly restrict their output space to the set of all single contiguous spans from the input, in order to alleviate the learning problem and…