Shared Task on Grammar Induction


This shared task aims to foster continuing research in grammar induction and part-of-speech induction, while also opening up the problem to more ambitious settings, including a wider variety of languages, removing the reliance on gold standard parts-of-speech and, critically, providing a thorough evaluation including a task-based evaluation.

The shared task will evaluate dependency grammar induction algorithms, evaluating the quality of structures induced from natural language text. In contrast with the defacto standard experimental setup, which starts with gold standard part-of-speech tags, we will encourage competitors to submit systems which are completely unsupervised. The evaluation will consider the standard dependency tree based measures (directed, undirected edge accuracy, bracketing accuracy etc) as well as measures over the predicted parts of speech. Our aim is to allow a wide range of different approaches, and for this reason we will accept submissions which predict just the dependency trees for gold PoS, just the PoS, or both jointly.

While our focus is on unsupervised approaches, we recognise that there has been considerable related research using semi-supervised learning, domain adaption, cross-lingual projection and other partially supervised methods for building syntactic models. We will support these kinds of systems, but require the participants to declare which external resources they have used. When presenting the results, we will split them into two sets: purely unsupervised approaches and those that have some form of external supervision.

Tracks

* Gold Tag Set - Dependency structures should be predicted using the gold tagset (universal or not?) * Induced POS - Use Induced Part-of-Speech tags instead of the gold tags. The number of induced tags can be chosen by each participant. * Open resources - Other resources can be used, such as parallel data.


Data

The data that we will provide will be collated from existing treebanks in a variety of different languages, domains and linguistic formalisms. This will give a diverse range of data upon which to test grammar induction algorithms yielding a deeper insight into the accuracy and shortcomings of different algorithms. Our multi-lingual setup will be designed to allow competitors to develop cross-lingual approaches for transferring syntactic knowledge between languages. To support these techniques, we will evaluate competing systems against the tag-set and its reduction into universal tag-set. Clustering approaches will be supported using the standard metrics for evaluating cluster identifiers, e.g., many-to-1, 1-1, VI etc.

Where possible, we intend to compile multiple annotations for the same sentences such that the effect of the choice of linguistic formalism or annotation procedure can be offset in the evaluation. This is a long-standing issue in parsing where many researchers evaluate only against the Penn Treebank, a setting which does not reflect many modern advances in linguistic theory from the last two decades. Overall this test set will form a significant resource for the evaluation of parsers and grammar induction algorithms, and help to reduce the field's continuing reliance on the Penn Treebank.


Evaluation

The evaluation will have two parts, 1) a linguistic evaluation against a tree-bank, and 2) a task-based evaluation, where we incorporate the predictions of grammar/POS induction models into a machine translation system. The exact form of the task-based evaluation is to be decided, but may involve features for reranking MT outputs or features for source-side reordering.


Baseline