REVIEW 2 cited by
Bag-of-Words as Target for Neural Machine Translation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Bag-of-Words as Target for Neural Machine Translation
read the original abstract
A sentence can be translated into more than one correct sentences. However, most of the existing neural machine translation models only use one of the correct translations as the targets, and the other correct sentences are punished as the incorrect sentences in the training stage. Since most of the correct translations for one sentence share the similar bag-of-words, it is possible to distinguish the correct translations from the incorrect ones by the bag-of-words. In this paper, we propose an approach that uses both the sentences and the bag-of-words as targets in the training stage, in order to encourage the model to generate the potentially correct sentences that are not appeared in the training set. We evaluate our model on a Chinese-English translation dataset, and experiments show our model outperforms the strong baselines by the BLEU score of 4.55.
Forward citations
Cited by 2 Pith papers
-
PubMedQA: A Dataset for Biomedical Research Question Answering
PubMedQA supplies 273k+ biomedical QA instances that require reasoning over research abstracts to produce yes/no/maybe answers.
-
Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation
Reinforce-NAT and FS-decoder retrieve target sequential information for non-autoregressive translation, yielding higher BLEU than baseline NAT while preserving fast decoding and approaching autoregressive quality.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.