Pre-meeting (Dong Nguyen).
Related paper: Automatic Analysis of Rhythmic Poetry with Applications to Generation and Translation.
Focus paper: Poetic Statistical Machine Translation: Rhyme and Meter
The related paper was on analysis of rhythmic poetry, with applications to generation and translation. While the focus paper briefly touches on the issue of finding which syllables in a word are stressed and resort to the CMU Dictionary, the related paper argues that the CMU dictionary is not sufficient. The meter for the text is known beforehand, thus the focus is to map a word to syllable-stress patterns. They propose to use Finite State Transducers to convert English words to stress patterns. Every word initially has transitions to all stress pattern subsequences of lengths 1 to 4. EM training is then used to train the model. Their data is augmented with external data to get more data for training, and they allow common alternative patterns to occur.
They also discuss possible applications such as poetry generation. However, they merely show an example of a produced text, and don't do a quantitative evaluation.
For translation, they use a cascade of weighted FST. Again, the evaluation is not quantitative. Furthermore, the experiment setup was probably somewhat uncommon where text was translated that was already in the training set.
For held out data the system had much more difficulty.