3.1k citations · 5.1k across the 43 of their papers we have counts for
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Learning Python Code Suggestion with a Sparse Pointer Network
Avishkar Bhoopchand, Tim Rocktäschel, Earl Barr +1
To enhance developer productivity, all modern integrated development environments (IDEs) include code suggestion functionality that proposes likely next tokens at the cursor. While…
emoji2vec: Learning Emoji Representations from their Description
Ben Eisner, Tim Rocktäschel, Isabelle Augenstein +2
Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings. There currently exist several public…
MuFuRU: The Multi-Function Recurrent Unit
Dirk Weissenborn, Tim Rocktäschel
Recurrent neural networks such as the GRU and LSTM found wide adoption in natural language processing and achieve state-of-the-art results for many tasks. These models are characte…
Generating Natural Language Inference Chains
Vladyslav Kolesnyk, Tim Rocktäschel, Sebastian Riedel
The ability to reason with natural language is a fundamental prerequisite for many NLP tasks such as information extraction, machine translation and question answering. To quantify…
Lifted Rule Injection for Relation Embeddings
Thomas Demeester, Tim Rocktäschel, Sebastian Riedel
Methods based on representation learning currently hold the state-of-the-art in many natural language processing and knowledge base inference tasks. Yet, a major challenge is how t…
Stance Detection with Bidirectional Conditional Encoding
Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos +1
Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", negative" or "neutral". Previous work has as…
Programming with a Differentiable Forth Interpreter
Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky +1
Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider t…