Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Addressing Exposure Bias With Document Minimum Risk Training: Cambridge
  at the WMT20 Biomedical Translation Task   The 2020 WMT Biomedical translation task evaluated Medline abstract
translations. This is a small-domain translation task, meaning limited relevant
training data with very distinct style and vocabulary. Models trained on such
data are susceptible to exposure bias effects, particularly when training
sentence pairs are imperfect translations of each other. This can result in
poor behaviour during inference if the model learns to neglect the source
sentence.
  The UNICAM entry addresses this problem during fine-tuning using a robust
variant on Minimum Risk Training. We contrast this approach with data-filtering
to remove `problem' training examples. Under MRT fine-tuning we obtain good
results for both directions of English-German and English-Spanish biomedical
translation. In particular we achieve the best English-to-Spanish translation
result and second-best Spanish-to-English result, despite using only single
models with no ensembling.

19
The categories of this paper are: eess
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol