90 citations · 427 across the 29 of their papers we have counts for
13 papers · 1 filter
COVR: A test-bed for Visually Grounded Compositional Generalization with real images
Ben Bogin, Shivanshu Gupta, Matt Gardner +1
While interest in models that generalize at test time to new compositions has risen in recent years, benchmarks in the visually-grounded domain have thus far been restricted to syn…
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…
Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills
Ori Yoran, Alon Talmor, Jonathan Berant
Models pre-trained with a language modeling objective possess ample world knowledge and language skills, but are known to struggle in tasks that require reasoning. In this work, we…
Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decomposition
Mor Geva, Tomer Wolfson, Jonathan Berant
Recent efforts to create challenge benchmarks that test the abilities of natural language understanding models have largely depended on human annotations. In this work, we introduc…
Memory-efficient Transformers via Top- Attention
Ankit Gupta, Guy Dar, Shaya Goodman +2
Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input le…
Question Decomposition with Dependency Graphs
Matan Hasson, Jonathan Berant
QDMR is a meaning representation for complex questions, which decomposes questions into a sequence of atomic steps. While state-of-the-art QDMR parsers use the common sequence-to-s…