38 research outputs found
MetaMT,a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation
Manipulating training data leads to robust neural models for MT
Sequence to Sequence Mixture Model for Diverse Machine Translation
Sequence to sequence (SEQ2SEQ) models often lack diversity in their generated
translations. This can be attributed to the limitation of SEQ2SEQ models in
capturing lexical and syntactic variations in a parallel corpus resulting from
different styles, genres, topics, or ambiguity of the translation process. In
this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that
improves both translation diversity and quality by adopting a committee of
specialized translation models rather than a single translation model. Each
mixture component selects its own training dataset via optimization of the
marginal loglikelihood, which leads to a soft clustering of the parallel
corpus. Experiments on four language pairs demonstrate the superiority of our
mixture model compared to a SEQ2SEQ baseline with standard or diversity-boosted
beam search. Our mixture model uses negligible additional parameters and incurs
no extra computation cost during decoding.Comment: 11 pages, 5 figures, accepted to CoNLL201
Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation
Traditional Neural machine translation (NMT) involves a fixed training
procedure where each sentence is sampled once during each epoch. In reality,
some sentences are well-learned during the initial few epochs; however, using
this approach, the well-learned sentences would continue to be trained along
with those sentences that were not well learned for 10-30 epochs, which results
in a wastage of time. Here, we propose an efficient method to dynamically
sample the sentences in order to accelerate the NMT training. In this approach,
a weight is assigned to each sentence based on the measured difference between
the training costs of two iterations. Further, in each epoch, a certain
percentage of sentences are dynamically sampled according to their weights.
Empirical results based on the NIST Chinese-to-English and the WMT
English-to-German tasks depict that the proposed method can significantly
accelerate the NMT training and improve the NMT performance.Comment: Revised version of ACL-201