2,097 research outputs found
Optimizing Differentiable Relaxations of Coreference Evaluation Metrics
Coreference evaluation metrics are hard to optimize directly as they are
non-differentiable functions, not easily decomposable into elementary
decisions. Consequently, most approaches optimize objectives only indirectly
related to the end goal, resulting in suboptimal performance. Instead, we
propose a differentiable relaxation that lends itself to gradient-based
optimisation, thus bypassing the need for reinforcement learning or heuristic
modification of cross-entropy. We show that by modifying the training objective
of a competitive neural coreference system, we obtain a substantial gain in
performance. This suggests that our approach can be regarded as a viable
alternative to using reinforcement learning or more computationally expensive
imitation learning.Comment: 10 pages. CoNL
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
Semantic role labeling (SRL) is the task of identifying the
predicate-argument structure of a sentence. It is typically regarded as an
important step in the standard NLP pipeline. As the semantic representations
are closely related to syntactic ones, we exploit syntactic information in our
model. We propose a version of graph convolutional networks (GCNs), a recent
class of neural networks operating on graphs, suited to model syntactic
dependency graphs. GCNs over syntactic dependency trees are used as sentence
encoders, producing latent feature representations of words in a sentence. We
observe that GCN layers are complementary to LSTM ones: when we stack both GCN
and LSTM layers, we obtain a substantial improvement over an already
state-of-the-art LSTM SRL model, resulting in the best reported scores on the
standard benchmark (CoNLL-2009) both for Chinese and English.Comment: To appear in EMNLP 201
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