AMR parsing is the task that maps a sentence to an AMR semantic graph
au...
Utterance rewriting aims to recover coreferences and omitted information...
Graph encoders in AMR-to-text generation models often rely on neighborho...
Text generation from AMR requires mapping a semantic graph to a string t...
Analyzing patterns in a sequence of events has applications in text anal...
Semiring parsing is an elegant framework for describing parsers by using...
Recent embedding-based methods in unsupervised bilingual lexicon inducti...
Text generation from AMR involves emitting sentences that reflect the me...
Medical relation extraction discovers relations between entity mentions ...
Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs. ...
We use the largest open repository of public speaking—TED Talks—to
predi...
Automated prediction of public speaking performance enables novel system...
It is intuitive that semantic representations can be useful for machine
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The task of linearization is to find a grammatical order given a set of
...
Multi-hop reading comprehension focuses on one type of factoid question,...
Cross-sentence n-ary relation extraction detects relations among n
entit...
The problem of AMR-to-text generation is to recover a text representing ...
Neural attention models have achieved great success in different NLP tas...
This paper addresses the task of AMR-to-text generation by leveraging
sy...
In this paper, we introduce a variation of the skip-gram model which joi...
Conventional word sense induction (WSI) methods usually represent each
i...
Most languages use the relative order between words to encode meaning
re...
Orthographic similarities across languages provide a strong signal for
p...
We describe a matrix multiplication recognition algorithm for a subset o...
We present a computational framework for automatically quantifying verba...
The speed of convergence of the Expectation Maximization (EM) algorithm ...