Speech Writing and Analysis: Emma Watson's …

Machine translation (MT) quality estimation (QE) is the task of predicting the quality of a translation produced by an MT system without having a reference translation. At the level of sentences, quality is usually estimated in terms of the effort required to fix the translation, trying to predict metrics such as translation error rate (TER) or post-editing time. When it comes to word level, QE is usually tackled as the task of identifying which words in the translation need to be replaced or deleted. The main advantage of word-level MT QE in front of MT sentence- or document-level MT QE is that it can be used to help post-editors to focus their attention on those parts of the translation that need to be fixed. However, with the current approach of only identifying the words that need to be fixed, post-editors using word-level MT QE could be disregarding missing words. In order to improve the performance of such systems, we propose an approach capable to identifying both the words that need to be deleted and the positions where one or more words need to be inserted. The work presented compares different types of simple neural network architectures that build on different sources of bilingual information in order to provide such predictions. The results obtained not only confirm the feasibility of the approach proposed, but also that a reasonably high performance on both tasks can be obtained using relatively simple architectures.

Qualitative data analysis: data display | Emma's blog

Annotation as Assessment | Education World

Debra Messing Blasts E! On Air During Golden Globes …

In this talk I will discuss two examples, sentence simplification and document summarization, that explore the hypothesis that tailoring the model with knowledge of the task structure and linguistic requirements leads to better performance. In the first part, I will propose a new sentence simplification task (split-and-rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. I will show that the semantically-motivated split model is a key factor in generating fluent and meaning preserving rephrasings.
In the second part, I will discuss the shortcomings of sequence-to-sequence abstractive methods for document summarization and show that an extractive summarization system trained to globally optimize a common summarization evaluation metric outperforms state-of-the-art extractive and abstractive systems in both automatic and extensive human evaluations.

‘The Fappening’ Continues: Nudes of Kim Kardashian, …

Behind the observed surface form of language exist underlying structures and themes, such as syntax, topic and utterance intent. In this talk, I will present some work which composes graphical models to learn underlying variables with powerful data likelihood functions to model the observed surface form. One such application is in open-domain dialogue modelling, where the latent variables capture the variation in the possible responses to a user utterance. We show that the latent variable approach generates more acceptable diverse output, as measured by human annotators. Another is extending topic models to instead learn topics underlying entire sentences, rather than just words. This lets the model learn topics which capture compositional meaning, which a standard word-level model has difficult doing.

Official YouTube channel for California-based singer/songwriter, Tiffany Alvord
The Voice Browser Working Group's mission is to support browsing the web by voice

Nov 27, 2013 · Hi Emma

W3C is teaming up with Jefferson University to improve the design of the specification template

Publications | Empathic Lighting

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