Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End
  Speech Translation   End-to-end speech translation, a hot topic in recent years, aims to translate
a segment of audio into a specific language with an end-to-end model.
Conventional approaches employ multi-task learning and pre-training methods for
this task, but they suffer from the huge gap between pre-training and
fine-tuning. To address these issues, we propose a Tandem Connectionist
Encoding Network (TCEN) which bridges the gap by reusing all subnets in
fine-tuning, keeping the roles of subnets consistent, and pre-training the
attention module. Furthermore, we propose two simple but effective methods to
guarantee the speech encoder outputs and the MT encoder inputs are consistent
in terms of semantic representation and sequence length. Experimental results
show that our model outperforms baselines 2.2 BLEU on a large benchmark
dataset.

22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci