51 citations · 51 across the 3 of their papers we have counts for
10 papers · 1 filter
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…
Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations
Jonathan Herzig, Peter Shaw, Ming-Wei Chang +3
Sequence-to-sequence (seq2seq) models are prevalent in semantic parsing, but have been found to struggle at out-of-distribution compositional generalization. While specialized mode…
Open Domain Question Answering over Tables via Dense Retrieval
Jonathan Herzig, Thomas Müller, Syrine Krichene +1
Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages. In this work, we tackle open-domain QA over t…
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…
A Summarization System for Scientific Documents
Shai Erera, Michal Shmueli-Scheuer, Guy Feigenblat +15
We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, explorat…
Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing
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 collect…