90 citations · 427 across the 29 of their papers we have counts for
14 papers · 1 filter
oLMpics -- On what Language Model Pre-training Captures
Alon Talmor, Yanai Elazar, Yoav Goldberg +1
Recent success of pre-trained language models (LMs) has spurred widespread interest in the language capabilities that they possess. However, efforts to understand whether LM repres…
On the Limits of Learning to Actively Learn Semantic Representations
Omri Koshorek, Gabriel Stanovsky, Yichu Zhou +2
One of the goals of natural language understanding is to develop models that map sentences into meaning representations. However, training such models requires expensive annotation…
Question Answering is a Format; When is it Useful?
Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi +2
Recent years have seen a dramatic expansion of tasks and datasets posed as question answering, from reading comprehension, semantic role labeling, and even machine translation, to…
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…
Global Reasoning over Database Structures for Text-to-SQL Parsing
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-…
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…