8,299 research outputs found
Sequence Labeling Parsing by Learning Across Representations
We use parsing as sequence labeling as a common framework to learn across
constituency and dependency syntactic abstractions. To do so, we cast the
problem as multitask learning (MTL). First, we show that adding a parsing
paradigm as an auxiliary loss consistently improves the performance on the
other paradigm. Secondly, we explore an MTL sequence labeling model that parses
both representations, at almost no cost in terms of performance and speed. The
results across the board show that on average MTL models with auxiliary losses
for constituency parsing outperform single-task ones by 1.14 F1 points, and for
dependency parsing by 0.62 UAS points.Comment: Proc. of the 57th Annual Meeting of the Association for Computational
Linguistics (ACL 2019). Revised version after fixing evaluation bu
Gradient-based Inference for Networks with Output Constraints
Practitioners apply neural networks to increasingly complex problems in
natural language processing, such as syntactic parsing and semantic role
labeling that have rich output structures. Many such structured-prediction
problems require deterministic constraints on the output values; for example,
in sequence-to-sequence syntactic parsing, we require that the sequential
outputs encode valid trees. While hidden units might capture such properties,
the network is not always able to learn such constraints from the training data
alone, and practitioners must then resort to post-processing. In this paper, we
present an inference method for neural networks that enforces deterministic
constraints on outputs without performing rule-based post-processing or
expensive discrete search. Instead, in the spirit of gradient-based training,
we enforce constraints with gradient-based inference (GBI): for each input at
test-time, we nudge continuous model weights until the network's unconstrained
inference procedure generates an output that satisfies the constraints. We
study the efficacy of GBI on three tasks with hard constraints: semantic role
labeling, syntactic parsing, and sequence transduction. In each case, the
algorithm not only satisfies constraints but improves accuracy, even when the
underlying network is state-of-the-art.Comment: AAAI 201
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