University of Cambridge > Talks.cam > NLIP Seminar Series > Multilingual Models for Distributed Semantics

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Multilingual Models for Distributed Semantics

Download to your calendar using vCal

If you have a question about this talk, please contact Tamara Polajnar .

In this talk I will present a technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings. These models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences, while maintaining sufficient distance between those of dissimilar sentences, using a form of noise-contrastive update.

A nice feature of these models is that they do not rely on word alignments or any syntactic information, making them easy to apply to a large number of diverse languages. I will briefly also describe an extension of this approach to learn semantic representations at the document level.

The talk will conclude with an analysis of these models and some empirical evaluation. Using several cross-lingual document classification tasks, I show that this approach can be used to learn semantically plausible, multilingual distributed representations.

This talk is part of the NLIP Seminar Series series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Clare Hall Meeting Room In person in the Max Perutz Lecture Theatre (CB2 0QH) and via Zoom, link: https://mrc-lmb-cam-ac-uk.zoom.us/j/96608781341?pwd=MG5QMXVmOStXcFVtalBqUlBYc0tEdz09 Average on Saturdays: 7840.72560976 kWh

Other talks

Rothschild Public Lecture: The Quantum Centenary: from Order to Chaos Group Work Title to be confirmed Title to be confirmed Tea and Coffee Break Toward affordable preventive healthcare
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity