instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
This algorithm finds the optimal number by minimising a cost function based on the eigenvector structure of the word similarity matrix. We refer the reader to the relevant literature for further details.

Results and Discussion
Question: What are the six target languages?
answer:
Answer with content missing: (3 Experimental Setup) We experiment with six target languages: French (FR), Brazilian Portuguese (PT), Italian (IT), Polish (PL), Croatian (HR), and Finnish (FI).

question:
The test-set accuracies obtained by different learning methods, including the current state-of-the-art results, are presented in Table TABREF11 .
Question: what models did they compare to?
answer:
High-order CNN, Tree-LSTM, DRNN, DCNN, CNN-MC, NBoW and SVM

question:
In this section, we first describe a baseline method inspired by the "align to segment" of BIBREF12, BIBREF13. We then propose two extensions providing the model with a signal relevant to the segmentation process, so as to move towards a joint learning of segmentation and alignment.
Question: Does the paper report any alignment-only baseline?
answer:
Yes