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Title: Semantic and Stylistic Variations in Machine Translation

Abstract: While parallel texts represent invaluable resources for machine translation, they inevitably introduce biases in the cross-lingual mappings learned by machine translation models.  In addition to the domain bias and translationese bias studied in past work, we argue that another form of bias arises from subtle choices in content and style made by translators to appropriately convey the meaning of the source to their target audience.

Title and Abstract: TBA 

Bio: Bert Huang is an assistant professor in the Virginia Tech Department of Computer Science. He investigates machine learning with a special focus on models and data with structure stemming from natural networks. Within this focus, his work addresses open questions on theory, algorithms, and applications.

Title- Interpretable Machine Learning: What it means, How we're getting there

The Ins and Outs of Preposition Semantics: Challenges of Coverage and Cross-linguistic Adequacy

Title: Information Abolition

Title: A Framework to Model Human Behavior at LargeScale during Natural Disasters

Title: From Linguistic Signal to Mental State: Computational Models of Framing

Theme 4: Flexible Speech Recognition



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