8,473 research outputs found
Better, Faster, Stronger Sequence Tagging Constituent Parsers
Sequence tagging models for constituent parsing are faster, but less accurate
than other types of parsers. In this work, we address the following weaknesses
of such constituent parsers: (a) high error rates around closing brackets of
long constituents, (b) large label sets, leading to sparsity, and (c) error
propagation arising from greedy decoding. To effectively close brackets, we
train a model that learns to switch between tagging schemes. To reduce
sparsity, we decompose the label set and use multi-task learning to jointly
learn to predict sublabels. Finally, we mitigate issues from greedy decoding
through auxiliary losses and sentence-level fine-tuning with policy gradient.
Combining these techniques, we clearly surpass the performance of sequence
tagging constituent parsers on the English and Chinese Penn Treebanks, and
reduce their parsing time even further. On the SPMRL datasets, we observe even
greater improvements across the board, including a new state of the art on
Basque, Hebrew, Polish and Swedish.Comment: NAACL 2019 (long papers). Contains corrigendu
Neural End-to-End Learning for Computational Argumentation Mining
We investigate neural techniques for end-to-end computational argumentation
mining (AM). We frame AM both as a token-based dependency parsing and as a
token-based sequence tagging problem, including a multi-task learning setup.
Contrary to models that operate on the argument component level, we find that
framing AM as dependency parsing leads to subpar performance results. In
contrast, less complex (local) tagging models based on BiLSTMs perform robustly
across classification scenarios, being able to catch long-range dependencies
inherent to the AM problem. Moreover, we find that jointly learning 'natural'
subtasks, in a multi-task learning setup, improves performance.Comment: To be published at ACL 201
- …