90 citations · 427 across the 29 of their papers we have counts for
14 papers · 1 filter
Transformer Feed-Forward Layers Are Key-Value Memories
Mor Geva, Roei Schuster, Jonathan Berant +1
Feed-forward layers constitute two-thirds of a transformer model's parameters, yet their role in the network remains under-explored. We show that feed-forward layers in transformer…
Improving Compositional Generalization in Semantic Parsing
Inbar Oren, Jonathan Herzig, Nitish Gupta +2
Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently. Specifically, compositional generalization, i.e., whether a model generalizes…
SmBoP: Semi-autoregressive Bottom-up Semantic Parsing
Ohad Rubin, Jonathan Berant
The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth…
Learning Object Detection from Captions via Textual Scene Attributes
Achiya Jerbi, Roei Herzig, Jonathan Berant +2
Object detection is a fundamental task in computer vision, requiring large annotated datasets that are difficult to collect, as annotators need to label objects and their bounding…
A Simple Global Neural Discourse Parser
Yichu Zhou, Omri Koshorek, Vivek Srikumar +1
Discourse parsing is largely dominated by greedy parsers with manually-designed features, while global parsing is rare due to its computational expense. In this paper, we propose a…
Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering
Ben Bogin, Sanjay Subramanian, Matt Gardner +1
Answering questions that involve multi-step reasoning requires decomposing them and using the answers of intermediate steps to reach the final answer. However, state-of-the-art mod…