Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Jointly Learning to Align and Translate with Transformer Models   The state of the art in machine translation (MT) is governed by neural
approaches, which typically provide superior translation accuracy over
statistical approaches. However, on the closely related task of word alignment,
traditional statistical word alignment models often remain the go-to solution.
In this paper, we present an approach to train a Transformer model to produce
both accurate translations and alignments. We extract discrete alignments from
the attention probabilities learnt during regular neural machine translation
model training and leverage them in a multi-task framework to optimize towards
translation and alignment objectives. We demonstrate that our approach produces
competitive results compared to GIZA++ trained IBM alignment models without
sacrificing translation accuracy and outperforms previous attempts on
Transformer model based word alignment. Finally, by incorporating IBM model
alignments into our multi-task training, we report significantly better
alignment accuracies compared to GIZA++ on three publicly available data sets.

29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th