This Tuesday, Micha Elsner will be presenting preliminary work on neural network word segmentation:
Given a corpus of phonemically transcribed utterances with unknown word boundaries, how can a cognitive model extract the vocabulary? I propose a new model based on working memory: the model must balance phonological memory (remembering how to pronounce words) with syntactic memory (remembering the utterance it just heard). Simulating the memory with encoder-decoder RNNs, I use reinforcement learning to optimize the segmentations.
Why build yet another model of word segmentation? (Is this simply a buzzword-compatibility issue? A little bit, but…) I hope to show that this model provides a deeper cognitive account of the prior biases used in previous work, and that its noisy, error-prone reconstruction process makes it inherently robust to variation in its input.
This is work in progress, so don’t expect great things from me yet. However, I will demonstrate model performance slightly worse than Goldwater et al 2009 on a standard dataset and discuss some directions for future work. Criticism, suggestions and thrown paper airplanes welcome.