95 citations · 138 across the 9 of their papers we have counts for
6 papers · 2 filters
Syntactic Scaffolds for Semantic Structures
Swabha Swayamdipta, Sam Thomson, Kenton Lee +3
We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, on…
Rational Recurrences
Hao Peng, Roy Schwartz, Sam Thomson +1
Despite the tremendous empirical success of neural models in natural language processing, many of them lack the strong intuitions that accompany classical machine learning approach…
Toward Abstractive Summarization Using Semantic Representations
Fei Liu, Jeffrey Flanigan, Sam Thomson +2
We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the sour…
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines
Roy Schwartz, Sam Thomson, Noah A. Smith
Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we presen…
Backpropagating through Structured Argmax using a SPIGOT
Hao Peng, Sam Thomson, Noah A. Smith
We introduce the structured projection of intermediate gradients optimization technique (SPIGOT), a new method for backpropagating through neural networks that include hard-decisio…
Learning Joint Semantic Parsers from Disjoint Data
Hao Peng, Sam Thomson, Swabha Swayamdipta +1
We present a new approach to learning semantic parsers from multiple datasets, even when the target semantic formalisms are drastically different, and the underlying corpora do not…