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Register a new userTowards Detection and Remediation of Phonemic Confusion
نحو كشف وعلاج الارتباك الصوتي
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يعد تقليل انهيار الاتصالات أمرا بالغ الأهمية للنجاح في تطبيقات NLP التفاعلية، مثل أنظمة الحوار.تحقيقا لهذه الغاية، نقترح إطارا لتخفيف الارتباك لكشف وعلاج انهيار الاتصالات.في هذا العمل، كخطوة أولى نحو تنفيذ هذا الإطار، نركز على الكشف عن مصادر الفونيم للارتباك.كدليل من المفهوم، نقوم بتقييم اثنين من المهندسين العصبيين في التنبؤ بالاحتمال الذي سيؤديه المستمع إلى إساءة فهم الصوت في الكلام.نظير على أن كلتا النماذج العصبية تتفوق على خط أساس نجم نجم مرجح، يظهر وعد مبكرا للإطار الأوسع.
Reducing communication breakdown is critical to success in interactive NLP applications, such as dialogue systems. To this end, we propose a confusion-mitigation framework for the detection and remediation of communication breakdown. In this work, as a first step towards implementing this framework, we focus on detecting phonemic sources of confusion. As a proof-of-concept, we evaluate two neural architectures in predicting the probability that a listener will misunderstand phonemes in an utterance. We show that both neural models outperform a weighted n-gram baseline, showing early promise for the broader framework.
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With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident. Previous works have demonstrated that KGs are prone to various social biases, a
nd have proposed multiple methods for debiasing them. However, in such studies, the focus has been on debiasing techniques, while the relations to be debiased are specified manually by the user. As manual specification is itself susceptible to human cognitive bias, there is a need for a system capable of quantifying and exposing biases, that can support more informed decisions on what to debias. To address this gap in the literature, we describe a framework for identifying biases present in knowledge graph embeddings, based on numerical bias metrics. We illustrate the framework with three different bias measures on the task of profession prediction, and it can be flexibly extended to further bias definitions and applications. The relations flagged as biased can then be handed to decision makers for judgement upon subsequent debiasing.
Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristi
cs are generally designed on the principle of selecting uncertain yet representative training instances, where annotating these instances may reduce a large number of errors. However, in an empirical study across six typologically diverse languages (German, Swedish, Galician, North Sami, Persian, and Ukrainian), we found the surprising result that even in an oracle scenario where we know the true uncertainty of predictions, these current heuristics are far from optimal. Based on this analysis, we pose the problem of AL as selecting instances that maximally reduce the confusion between particular pairs of output tags. Extensive experimentation on the aforementioned languages shows that our proposed AL strategy outperforms other AL strategies by a significant margin. We also present auxiliary results demonstrating the importance of proper calibration of models, which we ensure through cross-view training, and analysis demonstrating how our proposed strategy selects examples that more closely follow the oracle data distribution. The code is publicly released here.1
The 2020 US Elections have been, more than ever before, characterized by social media campaigns and mutual accusations. We investigate in this paper if this manifests also in online communication of the supporters of the candidates Biden and Trump, b
y uttering hateful and offensive communication. We formulate an annotation task, in which we join the tasks of hateful/offensive speech detection and stance detection, and annotate 3000 Tweets from the campaign period, if they express a particular stance towards a candidate. Next to the established classes of favorable and against, we add mixed and neutral stances and also annotate if a candidate is mentioned with- out an opinion expression. Further, we an- notate if the tweet is written in an offensive style. This enables us to analyze if supporters of Joe Biden and the Democratic Party communicate differently than supporters of Donald Trump and the Republican Party. A BERT baseline classifier shows that the detection if somebody is a supporter of a candidate can be performed with high quality (.89 F1 for Trump and .91 F1 for Biden), while the detection that somebody expresses to be against a candidate is more challenging (.79 F1 and .64 F1, respectively). The automatic detection of hate/offensive speech remains challenging (with .53 F1). Our corpus is publicly available and constitutes a novel resource for computational modelling of offensive language under consideration of stances.
Irony and Sentiment detection is important to understand people's behavior and thoughts. Thus it has become a popular task in natural language processing (NLP). This paper presents results and main findings in WANLP 2021 shared tasks one and two. The
task was based on the ArSarcasm-v2 dataset (Abu Farha et al., 2021). In this paper, we describe our system Multi-headed-LSTM-CNN-GRU and also MARBERT (Abdul-Mageed et al., 2021) submitted for the shared task, ranked 10 out of 27 in shared task one achieving 0.5662 F1-Sarcasm and ranked 3 out of 22 in shared task two achieving 0.7321 F1-PN under CodaLab username rematchka''. We experimented with various models and the two best performing models are a Multi-headed CNN-LSTM-GRU in which we used prepossessed text and emoji presented from tweets and MARBERT.
تلقى اختبار كشف الكذب اهتماما كبيرا منذ زمن بعيد و تتالت التقنيات و الادوات في كل حقبة تاريخة محاولة لانشاء نظام قادر على كشف كذب شخص من صدقه , لما لذلك من استخدامات عديدة أغلبها في كشف الحقيقة في الجرائم و المحاكمات و تنوعت تلك الادوات بداية من مراق
بة ردات معل الجسم و قياس المؤشرات الحيوية للشخص الى اجراء اختبارات نفسية على الاشخاص , و في الآونة الاخيرة تم التوجه الى تقنيات التعلم الآلي و الذكاء الصنعي و في بحثنا هذا نناقش عدة طرق في هذا الصدد "تحليل الكلام , تحليل حركة العيون, تحليل تعابير الوجه.
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