Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userParsiNLU: A Suite of Language Understanding Challenges for Persian
Parsinlu: مجموعة من اللغة فهم التحديات للفارسية
363
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
الملخص على الرغم من التقدم المحرز في السنوات الأخيرة في معالجة تحديات فهم اللغة الطبيعية (NLU)، فإن غالبية هذا التقدم لا يزال يتركز على اللغات الغنية بالموارد مثل اللغة الإنجليزية. يركز هذا العمل على اللغة الفارسية، واحدة من اللغات المنطوقة على نطاق واسع في العالم، ومع ذلك، هناك عدد قليل من مجموعات بيانات NLU متاحة لهذه اللغة. إن توفر مجموعات بيانات التقييم عالية الجودة ضرورة لتقييم موثوق للتقدم المحرز في مهام ومجال NLU المختلفة. نقدم Parsinlu، أول معيار باللغة الفارسية التي تتضمن مجموعة من مهام فهم اللغة --- قراءة الفهم والتتبع النصي وما إلى ذلك. يتم جمع مجموعات البيانات هذه في العديد من الطرق، وغالبا ما تنطوي على التعليقات التوضيحية اليدوية من قبل المتكلمين الأصليين. ينتج عن هذا أكثر من 14.5 ألف حالة جديدة عبر 6 مهام NLU مميزة. بالإضافة إلى ذلك، نقدم النتائج الأولى من طرازات اللغة أحادية الأحادية ومتعددة اللغات في هذه المعيار ومقارنتها بالأداء البشري، والذي يوفر رؤى قيمة في قدرتنا على معالجة التحديات الفارسية في اللغة الفارسية. نأمل أن يعزز Parsinlu مزيدا من البحث والتقدم في فهم اللغة الفارسية
Abstract Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of language understanding tasks---reading comprehension, textual entailment, and so on. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5k new instances across 6 distinct NLU tasks. Additionally, we present the first results on state-of-the-art monolingual and multilingual pre-trained language models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope ParsiNLU fosters further research and advances in Persian language understanding.1
References used
https://aclanthology.org/
rate research
Read More
This research deals with teaching Arabic as a second language. It
tackles the different characteristics and nationalities of learners in
addition to their objectives in relation to learning Arabic. This is taken
into consideration when preparing t
he required curricula from two
perspectives; the linguistic and the functional one.
This research sheds light on the role of technology that is utilized
to facilitate the task of learning Arabic by speakers of other languages in
relation to the pronunciation of letters, sounds, writing, grammatical
conjugation, comprehension and reading. The research also sheds light on
the most important challenges facing the Arabic Language since the
twenty first century such as the cultural challenge and the revival of local
and spoken dialects.
Supervised approaches usually achieve the best performance in the Word Sense Disambiguation problem. However, the unavailability of large sense annotated corpora for many low-resource languages make these approaches inapplicable for them in practice.
In this paper, we mitigate this issue for the Persian language by proposing a fully automatic approach for obtaining Persian SemCor (PerSemCor), as a Persian Bag-of-Word (BoW) sense-annotated corpus. We evaluated PerSemCor both intrinsically and extrinsically and showed that it can be effectively used as training sets for Persian supervised WSD systems. To encourage future research on Persian Word Sense Disambiguation, we release the PerSemCor in http://nlp.sbu.ac.ir.
In this paper, we propose a definition and taxonomy of various types of non-standard textual content -- generally referred to as noise'' -- in Natural Language Processing (NLP). While data pre-processing is undoubtedly important in NLP, especially wh
en dealing with user-generated content, a broader understanding of different sources of noise and how to deal with them is an aspect that has been largely neglected. We provide a comprehensive list of potential sources of noise, categorise and describe them, and show the impact of a subset of standard pre-processing strategies on different tasks. Our main goal is to raise awareness of non-standard content -- which should not always be considered as noise'' -- and of the need for careful, task-dependent pre-processing. This is an alternative to blanket, all-encompassing solutions generally applied by researchers through standard'' pre-processing pipelines. The intention is for this categorisation to serve as a point of reference to support NLP researchers in devising strategies to clean, normalise or embrace non-standard content.
This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We investigate two questions relate
d to the use of unlabeled data in production SSL context: 1) how to select samples from a huge unlabeled data pool that are beneficial for SSL training, and 2) how does the selected data affect the performance of different state-of-the-art SSL techniques. We compare four widely used SSL techniques, Pseudo-label (PL), Knowledge Distillation (KD), Virtual Adversarial Training (VAT) and Cross-View Training (CVT) in conjunction with two data selection methods including committee-based selection and submodular optimization based selection. We further examine the benefits and drawbacks of these techniques when applied to intent classification (IC) and named entity recognition (NER) tasks, and provide guidelines specifying when each of these methods might be beneficial to improve large scale NLU systems.
In this paper we discuss several challenges related to the development of a 3D game, whose goal is to raise awareness on cyberbullying while collecting linguistic annotation on offensive language. The game is meant to be used by teenagers, thus raisi
ng a number of issues that need to be tackled during development. For example, the game aesthetics should be appealing for players belonging to this age group, but at the same time all possible solutions should be implemented to meet privacy requirements. Also, the task of linguistic annotation should be possibly hidden, adopting so-called orthogonal game mechanics, without affecting the quality of collected data. While some of these challenges are being tackled in the game development, some others are discussed in this paper but still lack an ultimate solution.
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
