2 research outputs found
Word-based Domain Adaptation for Neural Machine Translation
In this paper, we empirically investigate applying word-level weights to
adapt neural machine translation to e-commerce domains, where small e-commerce
datasets and large out-of-domain datasets are available. In order to mine
in-domain like words in the out-of-domain datasets, we compute word weights by
using a domain-specific and a non-domain-specific language model followed by
smoothing and binary quantization. The baseline model is trained on mixed
in-domain and out-of-domain datasets. Experimental results on English to
Chinese e-commerce domain translation show that compared to continuing training
without word weights, it improves MT quality by up to 2.11% BLEU absolute and
1.59% TER. We have also trained models using fine-tuning on the in-domain data.
Pre-training a model with word weights improves fine-tuning up to 1.24% BLEU
absolute and 1.64% TER, respectively.Comment: Published on the proceedings of the International Workshop on Spoken
Language Translation (IWSLT), 201
Incremental Topic-Based Translation Model Adaptation for Conversational Spoken Language Translation
We describe a translation model adaptation approach for conversational spoken language translation (CSLT), which encourages the use of contextually appropriate translation options from relevant training conversations. Our approach employs a monolingual LDA topic model to derive a similarity measure between the test conversation and the set of training conversations, which is used to bias translation choices towards the current context. A significant novelty of our adaptation technique is its incremental nature; we continuously update the topic distribution on the evolving test conversation as new utterances become available. Thus, our approach is well-suited to the causal constraint of spoken conversations. On an English-to-Iraqi CSLT task, the proposed approach gives significant improvements over a baseline system as measured by BLEU, TER, and NIST. Interestingly, the incremental approach outperforms a non-incremental oracle that has up-front knowledge of the whole conversation.