#checkthat2020 検索結果
Evolution Team are presenting their participation in the Arabic tasks of #checkthat2020 lab at @clef_initiative #CLEF2020
#CLEF2020 #CheckThat2020 just wrapped up its sessions! Thanks for all the attendees and guest speakers for their interesting discussions and questions.
UB_ET team ranked third in task2 of #checkthat2020 lab are presenting how they used ad-hoc retrieval to retrieve previously fact-checked claims. @clef_initiative #CLEF2020
What should be done in fact-checking technology development in the near future, according to team Buster.al #checkthat2020 #clef2020
Our team member @MaramHasanain is presenting her work with @tamer_elsayed on detecting Arabic check-worthy tweets. #checkthat2020 #CLEF2020
Tamer Elsayed observes that #checkthat2020 has been evolving since 2020 in terms of tasks, datasets, and languages
After using the multilingual BERT to participate in the #checkthat2020 lab @MaramHasanain further investigated using AraBERT for the task and she achieved better results than the top team. #CLEF2020
@yskartal (w/@mucahidkutlu) is now describing the approach of team TOBB ETU. They decided to rely partially on AraBERT as well (btw, 41 people is attending #checkthat2020 right now)
@DCorney is introducing @FullFact. First key point: "bad information ruins lives". He underlines what fact-checkers want #checkthat2020
"Bad information ruins lives"... @DCorney starting his talk on claim matching at Full Fact fact-checking organization. #checkthat2020 @ #CLEF2020
@Fatabyyano_com, our third guest organisation, started by stressing that MENA is not one single area. Egypt, Middle East, Levant, etc. are quite different in terms of interests and online social dynamics #checkthat2020
Back to live: to close the participation of #checkthat2020 @clef_initiative 2020, @giodsm is about to present the tasks that we are preparing for the 2021 edition of the lab
Closing the 2nd session of #checkthat2020, @bigIR_group. They aimed at analysing how useful is mBERT compared to AraBERT. AraBERT is better, confirming the findings of the top-performing team Accenture. According to Maram, AraBERT is better mostly because it includes news
In his talk, @DCorney explains that current methods used for claim matching "works" or at least "better than nothing"... we aim with initiatives like #checkthat2020 to motivate development of *highly effective* automatic approaches.
UNIPI-NLE team are presenting their participation in task2 of #checkthat2020 lab @clef_initiative #CLEF2020
Edwin Thuma from team UB_ET is presenting their approach for verified claims retrieval. They built their retrieval engine around #Terrier. According to their results, #DPH weighting makes sense in this task #checkthat2020
Our team' model ranked the third in detecting Arabic check-worthy claims at #checkthat2020 lab. @MaramHasanain @tamer_elsayed
We are closing the first session of #checkthat2020 with @arkaitz Zubiaga presenting the QMUL-SDS approach The inclusion of rumour datasets does not seem to help when dealing with check-worthiness #clef2020
We are back with session 2 of #CheckThat2020 @clef_initiative #CELF2020. Team Buster.ai are presenting their work on verified claim retrieval. Transformers and pre-trained language models specifically are still going strong throughout the lab tasks!
The #clef2020 notes are out, including interesting approaches to fact checking presented at #checkthat2020. Thanks @frrncl
Back to live: to close the participation of #checkthat2020 @clef_initiative 2020, @giodsm is about to present the tasks that we are preparing for the 2021 edition of the lab
We thank once again the @DCorney, @rubenmiguez and M. Althaher for their participation in #checkthat2020. It was quite interesting to hear from the real fact-checking world and gratifying to learn that CT! is asking the right research questions
They have had some "fun" around COVID-19 fake news. Some of them include claims that people were killing each other massively in Italy during the lock down or that some religions were at higher risk of getting the illness... #checkthat2020
@Fatabyyano_com, our third guest organisation, started by stressing that MENA is not one single area. Egypt, Middle East, Levant, etc. are quite different in terms of interests and online social dynamics #checkthat2020
@rubenmiguez explains that, different from most other organisations in the field, newtral is a for-profit one. They are producers of some of the top journalist TV shows in Spain #checkthat2020
#CLEF2020 #CheckThat2020 just wrapped up its sessions! Thanks for all the attendees and guest speakers for their interesting discussions and questions.
In his talk, @DCorney explains that current methods used for claim matching "works" or at least "better than nothing"... we aim with initiatives like #checkthat2020 to motivate development of *highly effective* automatic approaches.
@DCorney is introducing @FullFact. First key point: "bad information ruins lives". He underlines what fact-checkers want #checkthat2020
"Bad information ruins lives"... @DCorney starting his talk on claim matching at Full Fact fact-checking organization. #checkthat2020 @ #CLEF2020
We are 5 mins away from the last session of this year's #checkthat2020 at 17:00, featuring @DCorney (@FullFact ), @rubenmiguez (@Newtral), and Moath Althaher (@fatabyyano_com) presenting interesting talks and discussions in a round table panel! #CLEF2020
The first @clef_initiative keynote is coming to an end. We are starting the last session of this year's #checkthat2020 at 17:00, featuring @DCorney (Full Fact), @rubenmiguez (Newtral), and Moath Althaher (Fatabyyano)
Añoro aquellos tiempos en los que #clef2020 y #sepln2020 eran en semanas distintas. Hoy es el día de #checkthat2020 y nos tendremos que perder #MEXA3T. ¡Mucha suerte!
After using the multilingual BERT to participate in the #checkthat2020 lab @MaramHasanain further investigated using AraBERT for the task and she achieved better results than the top team. #CLEF2020
Our team' model ranked the third in detecting Arabic check-worthy claims at #checkthat2020 lab. @MaramHasanain @tamer_elsayed
Our team member @MaramHasanain is presenting her work with @tamer_elsayed on detecting Arabic check-worthy tweets. #checkthat2020 #CLEF2020
Closing the 2nd session of #checkthat2020, @bigIR_group. They aimed at analysing how useful is mBERT compared to AraBERT. AraBERT is better, confirming the findings of the top-performing team Accenture. According to Maram, AraBERT is better mostly because it includes news
Evolution Team are presenting their participation in the Arabic tasks of #checkthat2020 lab at @clef_initiative #CLEF2020
According to EvolutionTeam (Ibtissam Touahri) sentiment is important for evidence retrieval. The more highly sentimental words appear, the more likely the text is an opinion (making it weaker as evidence) #checkthat2020 #clef2020
UB_ET team ranked third in task2 of #checkthat2020 lab are presenting how they used ad-hoc retrieval to retrieve previously fact-checked claims. @clef_initiative #CLEF2020
A total of 12 teams submitted predictions for the check-worthiness estimation task in English. Altogether, they explored an interesting number of models #checkthat2020
Evolution Team are presenting their participation in the Arabic tasks of #checkthat2020 lab at @clef_initiative #CLEF2020
UNIPI-NLE team are presenting their participation in task2 of #checkthat2020 lab @clef_initiative #CLEF2020
@Fatabyyano_com, our third guest organisation, started by stressing that MENA is not one single area. Egypt, Middle East, Levant, etc. are quite different in terms of interests and online social dynamics #checkthat2020
In his talk, @DCorney explains that current methods used for claim matching "works" or at least "better than nothing"... we aim with initiatives like #checkthat2020 to motivate development of *highly effective* automatic approaches.
Back to live: to close the participation of #checkthat2020 @clef_initiative 2020, @giodsm is about to present the tasks that we are preparing for the 2021 edition of the lab
The #clef2020 notes are out, including interesting approaches to fact checking presented at #checkthat2020. Thanks @frrncl
Closing the 2nd session of #checkthat2020, @bigIR_group. They aimed at analysing how useful is mBERT compared to AraBERT. AraBERT is better, confirming the findings of the top-performing team Accenture. According to Maram, AraBERT is better mostly because it includes news
"Bad information ruins lives"... @DCorney starting his talk on claim matching at Full Fact fact-checking organization. #checkthat2020 @ #CLEF2020
@DCorney is introducing @FullFact. First key point: "bad information ruins lives". He underlines what fact-checkers want #checkthat2020
@rubenmiguez explains that, different from most other organisations in the field, newtral is a for-profit one. They are producers of some of the top journalist TV shows in Spain #checkthat2020
@yskartal (w/@mucahidkutlu) is now describing the approach of team TOBB ETU. They decided to rely partially on AraBERT as well (btw, 41 people is attending #checkthat2020 right now)
The first @clef_initiative keynote is coming to an end. We are starting the last session of this year's #checkthat2020 at 17:00, featuring @DCorney (Full Fact), @rubenmiguez (Newtral), and Moath Althaher (Fatabyyano)
They have had some "fun" around COVID-19 fake news. Some of them include claims that people were killing each other massively in Italy during the lock down or that some religions were at higher risk of getting the illness... #checkthat2020
We thank once again the @DCorney, @rubenmiguez and M. Althaher for their participation in #checkthat2020. It was quite interesting to hear from the real fact-checking world and gratifying to learn that CT! is asking the right research questions
NLP&IR@UNED are presenting their participation in task1, task2, and task5 of #checkthat2020 lab at @clef_initiative. They adopted neural networks and graphs to solve the problems. #CLEF2020
And we are back in the second session of #checkthat2020 We start with Buster.ai, winners of task 2. M. Bouziane starts by stressing the large economic impact of fact checking
We are back with session 2 of #CheckThat2020 @clef_initiative #CELF2020. Team Buster.ai are presenting their work on verified claim retrieval. Transformers and pre-trained language models specifically are still going strong throughout the lab tasks!
Our team member @MaramHasanain is presenting her work with @tamer_elsayed on detecting Arabic check-worthy tweets. #checkthat2020 #CLEF2020
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