نتایج جستجو برای: domain adaptation
تعداد نتایج: 542537 فیلتر نتایج به سال:
We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neural summarization model benefits from pre-training based on extractive summaries. We also find that the combination of in-domain and out-of-domain s...
Automatic Annotation of Spoken Language Using Out-of-Domain Resources and Domain Adaptation
This paper studies a new, practical but challenging problem, called Class-Incremental Unsupervised Domain Adaptation (CI-UDA), where the labeled source domain contains all classes, classes in unlabeled target increase sequentially. problem is due to two difficulties. First, and label sets are inconsistent at each time step, which makes it difficult conduct accurate alignment. Second, previous u...
Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, yet related, domain for which no labeled data is available. This generalization across domains is a very significant challenge for many machine learning applications and arises in a variety of natural settings, includin...
Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, whilst test data is sampled from a target domain. This paper proposes and studies an approach, called feature-level domain adaptation (flda), that models the dependence between the two domains by means of a feature-leve...
Adversarial learning has been successfully embedded into deep networks to learn transferable features for domain adaptation, which reduce distribution discrepancy between the source and target domains and improve generalization performance. Prior domain adversarial adaptation methods could not align complex multimode distributions since the discriminative structures and inter-layer interactions...
We compare two different methods in domain adaptation applied to constituent parsing: parser combination and cotraining, each used to transfer information from the source domain of news to the target domain of natural dialogs, in a setting without annotated data. Both methods outperform the baselines and reach similar results. Parser combination profits most from the large amounts of training d...
Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can ...
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