| Abstract | In this paper, we evaluate performance on a domain adaptation setting where we translate sentences from the medical domain. |
| Abstract | Our experimental results show that ensemble decoding outperforms various strong baselines including mixture models, the current state-of-the-art for domain adaptation in machine translation. |
| Conclusion & Future Work | In this paper, we presented a new approach for domain adaptation using ensemble decoding. |
| Ensemble Decoding | Each of these mixture operations has a specific property that makes it work in specific domain adaptation or system combination scenarios. |
| Ensemble Decoding | For instance, LOPs may not be optimal for domain adaptation in the setting where there are two or more models trained on heterogeneous corpora. |
| Introduction | Domain adaptation techniques aim at finding ways to adjust an out-of-domain (OUT) model to represent a target domain (in-domain or IN). |
| Introduction | We expect domain adaptation for machine translation can be improved further by combining orthogonal techniques for translation model adaptation combined with language model adaptation. |
| Introduction | The main applications of ensemble models are domain adaptation , domain mixing and system combination. |
| Related Work 5.1 Domain Adaptation | Early approaches to domain adaptation involved information retrieval techniques where sentence pairs related to the target domain were retrieved from the training corpus using IR methods (Eck et al., 2004; Hildebrand et al., 2005). |
| Related Work 5.1 Domain Adaptation | Other domain adaptation methods involve techniques that distinguish between general and domain-specific examples (Daumé and Marcu, 2006). |
| Abstract | In this paper, we propose a domain adaptation framework for sentiment- and topic- lexicon co-extraction in a domain of interest where we do not require any labeled data, but have lots of labeled data in another related domain. |
| Abstract | Experimental results show that our domain adaptation framework can extract precise lexicons in the target domain without any annotation. |
| Introduction | To address this problem, we propose a two-stage domain adaptation method. |
| Introduction | While, most of previous work focused on document level; 2) A new two-step domain adaptation framework, with a novel RAP algorithm for seed expansion, is proposed. |
| Introduction | 2.2 Domain Adaptation |