Name
Abstract | ||
|---|---|---|
The CoNLL-2016 Shared Task is the second edition of the CoNLL-2015 Shared Task, now on Multilingual Shallow discourse parsing. Similar to the 2015 task, the goal of the shared task is to identify individual discourse relations that are present in natural language text. Given a natural language text, participating teams are asked to locate the discourse connectives (explicit or implicit) and their arguments as well as predicting the sense of the discourse connectives. Based on the success of the previous year, we continued to ask participants to deploy their systems on TIRA, a web-based platform on which participants can run their systems on the test data for evaluation. This evaluation methodology preserves the integrity of the shared task. We have also made a few changes and additions in the 2016 shared task based on the feedback from 2015. The first is that teams could choose to carry out the task on Chinese texts, or English texts, or both. We have also allowed participants to focus on parts of the shared task (rather than the whole thing) as a typical system requires substantial investment of effort. Finally, we have modified the scorer so that it can report results based on partial matches of the arguments. 23 teams participated in this year’s shared task, using a wide variety of approaches. In this overview paper, we present the task definition, the training and test sets, and the evaluation protocol and metric used during this shared task. We also summarize the different approaches adopted by the participating teams, and present the evaluation results. The evaluation data sets and the scorer will serve as a benchmark for future research on shallow discourse parsing. |
Year | Venue | Field |
|---|---|---|
2016 | CoNLL Shared Task | Ask price,Computer science,TIRA,Natural language,Natural language processing,Artificial intelligence,Test data,Parsing |
DocType | Citations | PageRank |
Conference | 18 | 0.72 |
References | Authors | |
27 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nianwen Xue | 1 | 141 | 5.67 |
| Hwee Tou Ng | 2 | 4092 | 300.40 |
| Sameer Pradhan | 3 | 1529 | 103.77 |
| Attapol Rutherford | 4 | 145 | 5.38 |
| Bonnie Lynn Webber | 5 | 1511 | 317.14 |
| Chuan Wang | 6 | 34 | 3.90 |
| Hongmin Wang | 7 | 19 | 2.49 |