From the 1 of 10 linked papers with an AI index.
521 citations · 618 across the 7 of their papers we have counts for
10 papers
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…
Few-shot Intent Classification and Slot Filling with Retrieved Examples
Dian Yu, Luheng He, Yuan Zhang +3
Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce…
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer
Evan Zheran Liu, Ramtin Keramati, Sudarshan Seshadri +4
Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables f…
REALM: Retrieval-Augmented Language Model Pre-Training
Kelvin Guu, Kenton Lee, Zora Tung +2
The paper introduces REALM, a language model that retrieves relevant documents from a large corpus during pre‑training and inference, enabling it to use external knowledge for task…
SPoC: Search-based Pseudocode to Code
Sumith Kulal, Panupong Pasupat, Kartik Chandra +4
We consider the task of mapping pseudocode to long programs that are functionally correct. Given test cases as a mechanism to validate programs, we search over the space of possibl…
Improving Semantic Parsing for Task Oriented Dialog
Arash Einolghozati, Panupong Pasupat, Sonal Gupta +4
Semantic parsing using hierarchical representations has recently been proposed for task oriented dialog with promising results [Gupta et al 2018]. In this paper, we present three d…