no code implementations • ACL 2020 • Alex Erdmann, er, Tom Kenter, Markus Becker, Christian Schallhart
Lexica distinguishing all morphologically related forms of each lexeme are crucial to many language technologies, yet building them is expensive.
no code implementations • 9 Sep 2019 • Rob Clark, Hanna Silen, Tom Kenter, Ralph Leith
We compare the results obtained from evaluating sentences in isolation, evaluating whole paragraphs of speech, and presenting a selection of speech or text as context and evaluating the subsequent speech.
no code implementations • 17 May 2019 • Vincent Wan, Chun-an Chan, Tom Kenter, Jakub Vit, Rob Clark
The prosodic aspects of speech signals produced by current text-to-speech systems are typically averaged over training material, and as such lack the variety and liveliness found in natural speech.
1 code implementation • 12 Oct 2018 • Hosein Azarbonyad, Mostafa Dehghani, Tom Kenter, Maarten Marx, Jaap Kamps, Maarten de Rijke
For measuring topical diversity of text documents, our HiTR approach improves over the state-of-the-art measured on PubMed dataset.
1 code implementation • 19 Dec 2017 • Tom Kenter, Maarten de Rijke
We argue that the process of building a representation of the conversation can be framed as a machine reading task, where an automated system is presented with a number of statements about which it should answer questions.
no code implementations • 13 Jul 2017 • Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra
Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them.
2 code implementations • ACL 2016 • Tom Kenter, Alexey Borisov, Maarten de Rijke
We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of high-quality sentence embeddings.