Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deeper Task-Specificity Improves Joint Entity and Relation Extraction   Multi-task learning (MTL) is an effective method for learning related tasks,
but designing MTL models necessitates deciding which and how many parameters
should be task-specific, as opposed to shared between tasks. We investigate
this issue for the problem of jointly learning named entity recognition (NER)
and relation extraction (RE) and propose a novel neural architecture that
allows for deeper task-specificity than does prior work. In particular, we
introduce additional task-specific bidirectional RNN layers for both the NER
and RE tasks and tune the number of shared and task-specific layers separately
for different datasets. We achieve state-of-the-art (SOTA) results for both
tasks on the ADE dataset; on the CoNLL04 dataset, we achieve SOTA results on
the NER task and competitive results on the RE task while using an order of
magnitude fewer trainable parameters than the current SOTA architecture. An
ablation study confirms the importance of the additional task-specific layers
for achieving these results. Our work suggests that previous solutions to joint
NER and RE undervalue task-specificity and demonstrates the importance of
correctly balancing the number of shared and task-specific parameters for MTL
approaches in general.

31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat