Those who remain script 2019 september registration#

The samples provided in the Data section have been clarified (training VS test/input samples). Additional information about fine-tuning the off-the-shelf models has been provided in the Data section. The evaluation datasets and instructions for submission have been sent to the participants. The task is now closed! 14 teams submitted outputs, the results of the automatic evaluations will be released in the next days.ДЄdditional information about the output specification has been added. The anonymized results of the automatic evaluations were released to the participants. The dates of the workshop have been confirmed, and the results and participating systems of the shared task will be presented on November 3rd at EMNLP-IJCNLP'19. The program of the day is now available on the MSR workshop page. Novem: Presentation of results and systems MSR workshop

Those who remain script 2019 september registration#

Note that due to a short time frame for the human evaluations, the date of the output collection will not be changed.ДЄpril 5, 2019: Registration for the task openДЄpril 5, 2019: Training and development sets released, evaluation scripts releasedДЄugust 19, 2019: System outputs collected (11:59 PM GMT -12:00) *No extension*ДЄugust 30, 2019: Automatic evaluation due Multiple datasets: for some languages, there are two or more UD datasets the teams are allowed to choose which dataset(s) they want to use for training their models.Relative word order information: relative word order information in Multiword Expressions, punctuations and multiple coordinations is available in the input.

Data alignment : in the training sets, the Shallow data is fully aligned with the original UD structures, and the Deep data is fully aligned with both the original and Shallow structures.For some languages, automatically parsed texts will also need to be generated. Evaluation: both in-domain and out-of-domain data can be used in the test data.Please contact the organizers to suggest resources a list will be maintained in the Data section. Furthermore, the use of some publicly available off-the-shelf language models suggested by the participants is allowed (e.g. However, it is allowed to use available parsers to create a silver standard version of the provided datasets and use them as additional/alternative training material. Closed task: in order to improve the comparability of the results, no other annotated data than those provided by the organizers can be used for training the systems.New features for the SR'19 (compared to SR'18):

In addition to what has to be done for the Shallow Track, the Deep Track thus consists in introducing the removed functional words and morphological features.Д®ither or both of the tracks can be addressed by the participating teams.

Deep Track (Track 2): This track starts from UD structures from which functional words (in particular, auxiliaries, functional prepositions and conjunctions) and surface-oriented morphological information have been removed.

The task consists in determining the word order and inflecting the words.

Shallow Track (Track 1): This track starts from genuine UD structures from which word order information has been removed and the tokens have been lemmatized, i.e., from unorderedДependency trees with lemmatized nodes that hold PoS tags and morphological information as found in the original annotations.

As in SRвЂ™18 and SR'11, the proposed shared task comprises two tracks with different levels of complexity: The goal of the shared task is to produce a well-formed sentence out of the given input structure. The task is endorsed by the ACL Special Interest Group on Natural Language Generation ( SIGGEN). The results of this second Multilingual Surface Realization Shared Task (SR'19) and the participating systems will be presented during the Multilingual Surface Realization Workshop 2019. The general objective of the Surface Realization Shared Task is to provide common-ground datasets for developing and evaluating Natural Language Generation systems.