2,449 research outputs found
Unsupervised Dependency Parsing: Let's Use Supervised Parsers
We present a self-training approach to unsupervised dependency parsing that
reuses existing supervised and unsupervised parsing algorithms. Our approach,
called `iterated reranking' (IR), starts with dependency trees generated by an
unsupervised parser, and iteratively improves these trees using the richer
probability models used in supervised parsing that are in turn trained on these
trees. Our system achieves 1.8% accuracy higher than the state-of-the-part
parser of Spitkovsky et al. (2013) on the WSJ corpus.Comment: 11 page
Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
The Abstract Meaning Representation (AMR) is a representation for open-domain
rich semantics, with potential use in fields like event extraction and machine
translation. Node generation, typically done using a simple dictionary lookup,
is currently an important limiting factor in AMR parsing. We propose a small
set of actions that derive AMR subgraphs by transformations on spans of text,
which allows for more robust learning of this stage. Our set of construction
actions generalize better than the previous approach, and can be learned with a
simple classifier. We improve on the previous state-of-the-art result for AMR
parsing, boosting end-to-end performance by 3 F on both the LDC2013E117 and
LDC2014T12 datasets.Comment: To appear in ACL 201
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding
Humans are remarkably flexible when understanding new sentences that include
combinations of concepts they have never encountered before. Recent work has
shown that while deep networks can mimic some human language abilities when
presented with novel sentences, systematic variation uncovers the limitations
in the language-understanding abilities of neural networks. We demonstrate that
these limitations can be overcome by addressing the generalization challenges
in a recently-released dataset, gSCAN, which explicitly measures how well a
robotic agent is able to interpret novel ideas grounded in vision, e.g., novel
pairings of adjectives and nouns. The key principle we employ is
compositionality: that the compositional structure of networks should reflect
the compositional structure of the problem domain they address, while allowing
all other parameters and properties to be learned end-to-end with weak
supervision. We build a general-purpose mechanism that enables robots to
generalize their language understanding to compositional domains. Crucially,
our base network has the same state-of-the-art performance as prior work, 97%
execution accuracy, while at the same time generalizing its knowledge when
prior work does not; for example, achieving 95% accuracy on novel
adjective-noun compositions where previous work has 55% average accuracy.
Robust language understanding without dramatic failures and without corner
causes is critical to building safe and fair robots; we demonstrate the
significant role that compositionality can play in achieving that goal
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