Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task
نادي 2021: مهمة الهوية العربية الدوارة الثانية
386
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نقدم نتائج ونتائج الهوية العربية ذات الدعوى الدقيقة Thesecond المهمة (NADI 2021).هذه المهام التجارية المشتركة أربعة مجموعات فرعية: تحديد الهوية العربي (SubTask1.1) على المستوى القطري (SubTask1.1)، وتحديد الهدوء على المستوى القطري (SubTAsk1.2)، وهوية MSA على مستوى المقاطعة (SubTask2.1) ولهجة فرعية من مستوى المقاطعةCountrifica-Tion (SubTask 2.2).مجموعة بيانات المهام المشتركة COV-ERS ما مجموعه 100 مقاطعة من 21 محاطة عربية، تم جمعها من مجال تويتر.تم تسجيل فريق TOTOROF 53 من 23 دولة في المحاسبة في المهام، مما يعكس مصلحة المجتمع في هذا المجال.تلقينا 16 حالة من الفئة الفرعية 1.1 من خمسة فرق، 27 حالة من الفئة الفرعية 1.2 من ثمانية فرق، 12 تقريرا ل SubTask 2.1 من أربعة فرق، و 13 طلبا ل SubTask 2.2 من Fourteams.
We present the findings and results of theSecond Nuanced Arabic Dialect IdentificationShared Task (NADI 2021). This Shared Taskincludes four subtasks: country-level ModernStandard Arabic (MSA) identification (Subtask1.1), country-level dialect identification (Subtask1.2), province-level MSA identification (Subtask2.1), and province-level sub-dialect identifica-tion (Subtask 2.2). The shared task dataset cov-ers a total of 100 provinces from 21 Arab coun-tries, collected from the Twitter domain. A totalof 53 teams from 23 countries registered to par-ticipate in the tasks, thus reflecting the interestof the community in this area. We received 16submissions for Subtask 1.1 from five teams, 27submissions for Subtask 1.2 from eight teams,12 submissions for Subtask 2.1 from four teams,and 13 Submissions for subtask 2.2 from fourteams.
References used
https://aclanthology.org/
rate research
Read More
This work investigates the value of augmenting recurrent neural networks with feature engineering for the Second Nuanced Arabic Dialect Identification (NADI) Subtask 1.2: Country-level DA identification. We compare the performance of a simple word-le
vel LSTM using pretrained embeddings with one enhanced using feature embeddings for engineered linguistic features. Our results show that the addition of explicit features to the LSTM is detrimental to performance. We attribute this performance loss to the bivalency of some linguistic items in some text, ubiquity of topics, and participant mobility.
This paper presents our approach to address the EACL WANLP-2021 Shared Task 1: Nuanced Arabic Dialect Identification (NADI). The task is aimed at developing a system that identifies the geographical location(country/province) from where an Arabic twe
et in the form of modern standard Arabic or dialect comes from. We solve the task in two parts. The first part involves pre-processing the provided dataset by cleaning, adding and segmenting various parts of the text. This is followed by carrying out experiments with different versions of two Transformer based models, AraBERT and AraELECTRA. Our final approach achieved macro F1-scores of 0.216, 0.235, 0.054, and 0.043 in the four subtasks, and we were ranked second in MSA identification subtasks and fourth in DA identification subtasks.
We present the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. This shared task comprises three binary classification subtasks with the goal to identify: toxic comments, engaging comments, and comments
that include indications of a need for fact-checking, here referred to as fact-claiming comments. Building on the two previous GermEval shared tasks on the identification of offensive language in 2018 and 2019, we extend this year's task definition to meet the demand of moderators and community managers to also highlight comments that foster respectful communication, encourage in-depth discussions, and check facts that lines of arguments rely on. The dataset comprises 4,188 posts extracted from the Facebook page of a German political talk show of a national public television broadcaster. A theoretical framework and additional reliability tests during the data annotation process ensure particularly high data quality. The shared task had 15 participating teams submitting 31 runs for the subtask on toxic comments, 25 runs for the subtask on engaging comments, and 31 for the subtask on fact-claiming comments. The shared task website can be found at https://germeval2021toxic.github.io/SharedTask/.
We report the results of the WMT 2021 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels. This edition focused on two main novel additio
ns: (i) prediction for unseen languages, i.e. zero-shot settings, and (ii) prediction of sentences with catastrophic errors. In addition, new data was released for a number of languages, especially post-edited data. Participating teams from 19 institutions submitted altogether 1263 systems to different task variants and language pairs.
This paper presents the Bering Lab's submission to the shared tasks of the 8th Workshop on Asian Translation (WAT 2021) on JPC2 and NICT-SAP. We participated in all tasks on JPC2 and IT domain tasks on NICT-SAP. Our approach for all tasks mainly focu
sed on building NMT systems in domain-specific corpora. We crawled patent document pairs for English-Japanese, Chinese-Japanese, and Korean-Japanese. After cleaning noisy data, we built parallel corpus by aligning those sentences with the sentence-level similarity scores. Also, for SAP test data, we collected the OPUS dataset including three IT domain corpora. We then trained transformer on the collected dataset. Our submission ranked 1st in eight out of fourteen tasks, achieving up to an improvement of 2.87 for JPC2 and 8.79 for NICT-SAP in BLEU score .
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
