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SPMRL 2014 Shared Task Description


The primary goal of the First Shared Task on Parsing Morphologically-Rich Languages was to bring forward work on parsing morphologically ambiguous input in both dependency and constituency parsing, and to show the state of the art for MRLs. In the longer term, we aim to provide streamlined data sets and evaluation metrics, thus improving the comparability of cross linguistic work on parsing MRLs.

The 2014 Shared Task edition will explicitly allow and favor the use of large unlabeled data set. In order to correctly evaluate the improvement brought by the use of semi-supervized models, all annotated data and evaluation process will remain the same.

The shared task features tracks in constituency parsing and in dependency parsing, in gold as well as in realistic scenarios (the realistic scenario has no gold tokenization, no gold part-of-speech tags and morphological features).

Data Set

For the shared task, we have nine different treebanks: Arabic, Basque, French, German, Hebrew, Hungarian, Korean, Polish, and Swedish.

Although not always represented in treebanks, most languages have phenomena where space delimited tokens do not correspond to words as active ingredients in syntax. This covers multi-word expressions such as in spite of in English, but also phenomena in Arabic, where conjunctions are attached to the following word.

In order to provide the means to produce and evaluate more realistic parsing models, whenever it's possible, we provide data following different tokenization schemes: one with word gold segmentation, and one in which we have unsegmented text as it would appear in newspapers, etc.

For all these treebanks, we provide Unlabeled data set as well. To lower the entry cost for new comers in the field, we also provide more than accurate baseline, if not state-of-the-art, morpho-syntactic annotations (POS tagged, morphological features, lemmas and multiword expressions if available in the original treebank) and syntactic dependencies.
Please note that the use of the provided annotation is absolutely not mandatory.

Parsing Scenarios

We have two syntactic frameworks:

  1. constituent structure
  2. dependency structure

Constituent structures are available in two formats: an extended PTB bracketed style (eg Penn Treebank with morphological features expressed at the POS or non terminal levels, see below) and, if available, the Tiger 2 format. The latter has the possibility to represent trees with crossing branches, allowing the use of more powerful parsing models (in term of expressivity) than pure PCFG-based parsers.

Dependency structures are available in the CoNLL'07 format.

All treebank instances (dep. and const.) are aligned at the token level and share the same POS tagset and morphological features.

Participants can choose either one of those frameworks, or both, or one by conversion from the other.

Input scenarios

The types of scenarios that we assume in terms of input are as follows:

  1. Gold tags: gold word segmentation and gold tags are given
  2. Predicted tags, gold segmentation: gold word segmentation is given (for the languages where it matters, otherwise standard words), POS tags and morph. features are automatically predicted (available for Basque, German, Hungarian, Korean, Polish, Swedish)
  3. Fully Predicted: sentences are tokenized; raw words are given (available for French, Arabic and Hebrew)

The evaluation task focuses on the last two scenarios but participants are strongly encouraged to provide “gold mode” parsing results so that a performance ceiling can be determined for each system/framework.

Training set size scenario

Our data set contains treebanks with different size (from 6k to 50k sentences). So in order to allow for a fair comparison between treebank/parsing model pairs, we also provide training sets with a common size of 5000 sentences. Participants should thus also provide results from parsing models trained on the small data set.

To sum up, we have different scenarios with regard to the size of the training sets:

  • the full training set
  • a 5000 sentence training set
  • the full test set will be used for both scenarios, we will later sample a common subset with similar properties in term of sentence length and number of tokens.

Input Formats

The input format is a variant of the CoNLL format for dependencies. This is necessary to represent word segmentation issues and easily allow to include morphological features and alternative analysis. We mark the beginning and the end of words, which do not have to correspond to what we call tokens, which can consist of more than one word.

  • In Hebrew, where several (syntactically important) morphemes create a word, we will have something like the following: hebrewIn.pdf
  • Note that if one wants to deliver a lattice in which segmentation is ambiguous, they can do so by adding lines for alternative spans or alternative tags of spans. These lines need not be sorted. See the (real-world) example segmentation lattice here: multi.pdf
  • or the German morphology lattice file (predicted from the SMOR analyser):
  • 0 1 Der PRELS gender=fem|case=dat|number=sg| 1
  • 0 1 Der PRELS gender=masc|case=nom|number=sg| 1
  • 0 1 Der PDS gender=fem|case=dat|number=sg| 1

The format of Form/Lemma/CPos/FPos/Feats is the exact same as in the CoNLL format, including vertical bars separating morphemes, and = separate feature values. The only additional value in addition to the CoNLL ones is the original token ID in the last column.

Output Formats

In order to evaluate all scenarios, we consider the terminals present in both the trees, and we will need to keep track of how the given parse is related to the original word tokens (this is true both for parsing over a segmentation lattice or for MWEs)

  • For dependency trees, the parsers are to deliver the standard CoNLL format
  • For constituency trees we require the standard PTB-like trees over terminals (one tree per line)

For the scenario based on fully raw text, we additionally require a file containing the token IDs. That is, for the Hebrew sentence of BCLM HNEIM, after disambiguation as follows B CL FL HM H NEIM, we would get a (constituent/ dependency) tree for these 6 morphological segments, and in a parallel file, we would get the following line:

1 1 1 1 2 2

saying that the first 4 leaves in the syntax structure correspond to the first word in the raw text, and the last 2 leaves correspond to the second word.

This may also be used for multiword expressions: for “I live in Tel Aviv” we would get the tree as usual over the 5 terminals, and the line

1 2 3 4 4

In all cases, the tree terminals hang separately under their parents (in the case of a MWE they will hang flat under a shared parent).

Evaluating All Scenarios

  • For constituent evaluation on gold word segmentation of bracketed output (eg. PTB), we use a modified version of Parseval's evalb: evalb2013.tar.gz. Add -fPIC to gcc to compile for Linux.

update *February 2014: the evalb package that was available on Djame's site was not the correct one. if your version doesn't have the -X switch, it's the buggy one.

  • MWE Evaluation
    On top of classical evalb and evaluation, we will also provide results on multiword expression (For now only for French.). Thanks to Marie Candito, the evaluator for dependencies output is provided on tools (see test/tools/ or here).
    In the very next days, we'll provide the same script for mwe eval of constituency parses, however here's the readme of the current tool].
shared_task_description.txt · Last modified: 2015/02/20 19:12 by seddah