updated readme

Author: emanjavacas

README.md

@@ -1,12 +1,13 @@
-# pie
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+# PIE: A Framework for Joint Learning of Sequence Labelling Tasks
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+PIE was primarily conceived to make experimentation on sequence labelling of variation-rich languages easy and user-friendly. PIE has been tested mostly for Lemmatization but other SoTA accuracies from other tasks like POS have been reproduced (cf. Plank et al ). PIE is *highly* configurable both in terms of input preprocessing and model definition, in principle not requiring users to write any code (instead experiments are defined with json files). It is highly modular and therefore easy to extend. It includes transductive lemmatization as an additional sequence labelling task and, finally, it is reasonably fast.
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+Documentation is work in progress and it will improve over the following months, for now the best is to check `pie/default_settings.json` which explains all input parameters and shows a full example of a config file.
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+Model description and evaluation results are also in preparation.
 
 - Future work:
   - Add GRL regularization on domain/source labels (which seems to help POS [https://arxiv.org/pdf/1805.06093.pdf](here: Table 1&2). We can use file names, or derive appropriate labels from file names.
-  - Train both linear and char-level decoders.
-  - Fine-tune threshold on dev data to see when to pick which output. 
   - Implement a mixture-model to learn to decide whether to retrieve a lemma from the cache or go and generate it one character at a time [https://arxiv.org/pdf/1609.07843.pdf](similar to this paper).
-  - Morphology: people seem to just run same kind of linear classifier (one per label)
-  - In what sense is POS not a morphology-label like NUM, TENSE, etc...
-  - Fine-grain error analyses:
-	- classify lemmatas based on edit-distance with the source (different bins, show accuracy per bin)
-	- does contextual information help for lemmatization (compare to a baseline where decoding is done char-level but unconditionally, compare to linear decoding baseline without contextual information)?
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