2.2k citations · 5k across the 31 of their papers we have counts for
3 papers · 1 filter
Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs
Jean Maillard, Stephen Clark, Dani Yogatama
We introduce a neural network that represents sentences by composing their words according to induced binary parse trees. We use Tree-LSTM as our composition function, applied alon…
Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems
Wang Ling, Dani Yogatama, Chris Dyer +1
Solving algebraic word problems requires executing a series of arithmetic operations---a program---to obtain a final answer. However, since programs can be arbitrarily complicated,…
Generative and Discriminative Text Classification with Recurrent Neural Networks
Dani Yogatama, Chris Dyer, Wang Ling +1
We empirically characterize the performance of discriminative and generative LSTM models for text classification. We find that although RNN-based generative models are more powerfu…