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ORGANIZER;CN=Eric Atwell:mailto:[email protected]
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=corpora@li
 st.elra.info:mailto:[email protected]
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=DES Course
  Support:mailto:[email protected]
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=sigarab@go
 oglegroups.com:mailto:[email protected]
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=IDH Networ
 k:mailto:[email protected]
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Eric Atwel
 l:mailto:[email protected]
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=James Dick
 ins:mailto:[email protected]
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Arabic-l@l
 ists.leeds.ac.uk:mailto:[email protected]
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=comp-staff
 :mailto:[email protected]
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=School of 
 Computer Postgraduate Researchers:mailto:[email protected]
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=rana.malha
 [email protected]:mailto:[email protected]
DESCRIPTION;LANGUAGE=en-GB:Please share this invitation with your network\n
 \nthanks\n\nEric Atwell\, School of Computing\, University of Leeds\n\n___
 _____________________________\n\nSchool of Computing Colloquium\, Universi
 ty of Leeds\, Friday 24.2.23 12:00 (UK)\n\nGuest speaker: Dr Rana Malhas\,
  Qatar University\n\nTitle: Arabic Reading Comprehension on the Holy Qur
 ’an using CL-AraBERT\n\nVenue:  Teams Click here to join the meeting<htt
 ps://emea01.safelinks.protection.outlook.com/ap/t-59584e83/?url=https%3A%2
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 d=0>\n\nAbstract: In this talk\, we tackle the problem of machine reading 
 comprehension (MRC) on the Holy Qur’an to address the lack of Arabic dat
 asets and systems for this important task. We construct QRCD as the first 
 Qur’anic Reading Comprehension Dataset\, composed of 1\,337 question pas
 sage-answer triplets for 1\,093 question-passage pairs\, of which 14% are 
 multi-answer questions. We then introduce CLassical-AraBERT (CL-AraBERT fo
 r short)\, a new AraBERTbased pre-trained model\, which is further pre-tra
 ined on about 1.0B-word Classical Arabic (CA) dataset\, to complement the 
 Modern Standard Arabic (MSA) resources used in pre-training the\ninitial m
 odel\, and make it a better fit for the task. Finally\, we leverage cross-
 lingual transfer learning from MSA to CA\, and fine-tune CL-AraBERT as a r
 eader using two MSA-based MRC datasets followed by our QRCD dataset to con
 stitute the first (to the best of our knowledge) MRC system on the Holy Qu
 r’an. To evaluate our system\, we introduce Partial Average Precision (
 𝑝𝐴𝑃 ) as an adapted version of the traditional rank-based Average
  Precision measure\, which integrates partial matching in the evaluation o
 ver multi-answer and single-answer MSA questions. Adopting two experimenta
 l evaluation setups (hold-out and cross validation (CV))\, we empirically 
 show that the fine-tuned CL-AraBERT reader model significantly outperforms
 \nthe baseline fine-tuned AraBERT reader model by 6.12 and 3.75 points in 
 𝑝𝐴𝑃 scores\, in the hold-out and CV setups\, respectively. To pro
 mote further research on this task and other related tasks on Qur’an and
  Classical Arabic text\, we make both the QRCD dataset and the pre-trained
  CL-AraBERT model publicly available.\n\n_________________________________
 _______________________________________________\nMicrosoft Teams meeting\n
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 ______________________________________________________________\n
UID:040000008200E00074C5B7101A82E008000000001B2A946A6740D901000000000000000
 010000000D72F21B22B96F64691984C744D230379
SUMMARY;LANGUAGE=en-GB:Rana Malhas\, Arabic Reading Comprehension on the Ho
 ly Qur’an using CL-AraBERT 24.2.23 12:00 
DTSTART;TZID=GMT Standard Time:20230224T120000
DTEND;TZID=GMT Standard Time:20230224T130000
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20230214T112813Z
TRANSP:OPAQUE
STATUS:CONFIRMED
SEQUENCE:0
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