posted by organizer: manandinfo || 9412 views || tracked by 1 users: [display]

DPIL @ FIRE 2016 : Shared Task on Detecting Paraphrase in Indian Languages (DPIL) held in conjunction with FIRE2016


When Dec 8, 2016 - Dec 10, 2016
Where ISI, Kolkatta
Submission Deadline TBD
Categories    natural language processing   indian languages   NLP   semantic similarity

Call For Papers

Call for Participation
CFP in Shared Task on Detecting Paraphrase in Indian Languages (DPIL) held in conjunction with the Forum for Information Retrieval Evaluation (FIRE) 2016 on 8th - 10th December 2016, Kolkata

Paraphrase can be defined as “the same meaning of a sentence is expressed in another sentence using different words”. Paraphrases can be identified, generated or extracted. The proposed task is focused on sentence level paraphrase identification for Indian languages (Tamil, Malayalam, Hindi and Punjabi). Identifying paraphrases in Indian languages is a difficult task, because evaluating the semantic similarity of the underlying content and the understanding the morphological variations of the language are more critical. Paraphrase identification is strongly connected with generation and extraction of paraphrases. The paraphrase identification systems improve the performance of a paraphrase generation in terms of choosing the best paraphrase candidate from the list of paraphrases candidates generated by paraphrases generation system. Paraphrase Identification is also used in validating the paraphrase extraction system and the machine translation system. Since there are no annotated corpora or automated semantic interpretation systems available for Indian languages till date, creating benchmark data for paraphrases and utilizing that data in open shared task competitions will motivate the research community for further research in Indian languages.

Sub Task 1:

Given a pair of sentences from news paper domain, the task is to classify them as paraphrases (P) or not paraphrases (NP).

Sub Task 2:

Given two sentences from news paper domain, the task is to identify whether they are completely equivalent (E) or roughly equivalent (RE) or not equivalent (NE). This task is similar to the subtask 1, but the main difference is 3-point scale tag in paraphrases.


We are planning to provide cash awards or travel grants for Top 2 winning teams . We glad to invite all researchers working on Paraphrase Identification, Plagiarism detection and Machine Translation Evaluation to participate in the Shared Task DPIL.

Important Dates
21th June 2016 : Registration Starts
10th July 2016 : Training data will be Released
15th August 2016 : Test Data will be Released
1st September 2016 : Deadline of Runs Submission
10th September 2016 : Results declared
10th October 2016 : Working Notes Due
December 8-10, 2016 : FIRE 2016 Conference

-------------------------- Registeration -------------------------
Registration is now open. You can register on the DPIL website:
Task website :
Fire website :

Contact email:
Track Organizers
Anand Kumar M, CEN, Amrita Vishwa Vidyapeetham, Coimbatore, India
Soman K P , CEN, Amrita Vishwa Vidyapeetham, Coimbatore, India

Related Resources

EMNLP-IJCNLP 2019   Conference on Empirical Methods in Natural Language Processing & International Joint Conference on Natural Language Processing 2019
#SMM4H 2019   #SMM4H: Social Media Mining for Health Applications Workshop & Shared Task at ACL 2019
LTA 2019   4th International Workshop on Language Technologies and Applications
FinSBD-2019 Shared Task 2019   [IJCAI-2019] Call for participation: FinSBD-2019 Shared Task - Sentence Boundary Detection in PDF Noisy Text in the Financial Domain
CONLL 2019   The SIGNLL Conference on Computational Natural Language Learning
SlavicNER @ BSNLP 2019   BSNLP-2019 2nd Edition of the Shared Task on Multilingual Named Entity Recognition for Slavic languages
RANLP 2019   Recent Advances in Natural Language Processing
GermEval Task 1 2019   Shared Task on hierarchical classification of German Blurbs
ICNLSP 2019   3 rd International Conference on Natural Language and Speech Processing