Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userComparing Approaches to Dravidian Language Identification
مقارنة النهج لتحديد لغة Dravidian
675
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تصف هذه الورقة التقديمات من قبل Team HWR إلى المهمة المشتركة (DLI) التي تم تنظيمها في ورشة عمل فاريدية 2021. تتضمن مجموعة تدريب DLI 16،674 تعليقات يوتيوب مكتوبة في البرنامج النصي الروماني يحتوي على نص مختلط من التعليمات البرمجية مع اللغة الإنجليزية واحدا من اللغات الثلاثة الجنوبية الدرفيدية: كانادا، ملايال، والتاميل. لقد قدمنا النتائج التي تم إنشاؤنا باستخدام نموذجين، وهو مصنف بايز ساذج مع نماذج بلغة تكيفية، والتي أثبتت الحصول على أداء تنافسي في العديد من مهام تحديد اللغة واللهجة، وعلى نموذج يستند إلى المحولات الذي يعتبر على نطاق واسع كحدث الفن في عدد من مهام NLP. تم إرسال طلبنا الأول في مسار التقديم المغلق باستخدام مجموعة التدريب فقط المقدمة من منظمي المهام المشتركة، في حين أن التقديم الثاني يعتبر مفتوحا لأنه استخدم نموذجا مسبقا تدريبا على البيانات الخارجية. حصل فريقنا على المركز الثاني المشترك في المهمة المشتركة مع التقديم على أساس بايس ساذج. تعزز نتائجنا فكرة أن أساليب التعلم العميقة ليست تنافسية في المهام المتعلقة بتحديد اللغة لأنها في العديد من مهام تصنيف النص الأخرى.
This paper describes the submissions by team HWR to the Dravidian Language Identification (DLI) shared task organized at VarDial 2021 workshop. The DLI training set includes 16,674 YouTube comments written in Roman script containing code-mixed text with English and one of the three South Dravidian languages: Kannada, Malayalam, and Tamil. We submitted results generated using two models, a Naive Bayes classifier with adaptive language models, which has shown to obtain competitive performance in many language and dialect identification tasks, and a transformer-based model which is widely regarded as the state-of-the-art in a number of NLP tasks. Our first submission was sent in the closed submission track using only the training set provided by the shared task organisers, whereas the second submission is considered to be open as it used a pretrained model trained with external data. Our team attained shared second position in the shared task with the submission based on Naive Bayes. Our results reinforce the idea that deep learning methods are not as competitive in language identification related tasks as they are in many other text classification tasks.
References used
https://aclanthology.org/
rate research
Read More
Dravidian languages, such as Kannada and Tamil, are notoriously difficult to translate by state-of-the-art neural models. This stems from the fact that these languages are morphologically very rich as well as being low-resourced. In this paper, we fo
cus on subword segmentation and evaluate Linguistically Motivated Vocabulary Reduction (LMVR) against the more commonly used SentencePiece (SP) for the task of translating from English into four different Dravidian languages. Additionally we investigate the optimal subword vocabulary size for each language. We find that SP is the overall best choice for segmentation, and that larger dictionary sizes lead to higher translation quality.
The use of attention mechanisms in deep learning approaches has become popular in natural language processing due to its outstanding performance. The use of these mechanisms allows one managing the importance of the elements of a sequence in accordan
ce to their context, however, this importance has been observed independently between the pairs of elements of a sequence (self-attention) and between the application domain of a sequence (contextual attention), leading to the loss of relevant information and limiting the representation of the sequences. To tackle these particular issues we propose the self-contextualized attention mechanism, which trades off the previous limitations, by considering the internal and contextual relationships between the elements of a sequence. The proposed mechanism was evaluated in four standard collections for the abusive language identification task achieving encouraging results. It outperformed the current attention mechanisms and showed a competitive performance with respect to state-of-the-art approaches.
Extracting the most important part of legislation documents has great business value because the texts are usually very long and hard to understand. The aim of this article is to evaluate different algorithms for text summarization on EU legislation
documents. The content contains domain-specific words. We collected a text summarization dataset of EU legal documents consisting of 1563 documents, in which the mean length of summaries is 424 words. Experiments were conducted with different algorithms using the new dataset. A simple extractive algorithm was selected as a baseline. Advanced extractive algorithms, which use encoders show better results than baseline. The best result measured by ROUGE scores was achieved by a fine-tuned abstractive T5 model, which was adapted to work with long texts.
Deep neural networks and huge language models are becoming omnipresent in natural language applications. As they are known for requiring large amounts of training data, there is a growing body of work to improve the performance in low-resource settin
gs. Motivated by the recent fundamental changes towards neural models and the popular pre-train and fine-tune paradigm, we survey promising approaches for low-resource natural language processing. After a discussion about the different dimensions of data availability, we give a structured overview of methods that enable learning when training data is sparse. This includes mechanisms to create additional labeled data like data augmentation and distant supervision as well as transfer learning settings that reduce the need for target supervision. A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific low-resource setting. Further key aspects of this work are to highlight open issues and to outline promising directions for future research.
Finding the year of writing for a historical text is of crucial importance to historical research. However, the year of original creation is rarely explicitly stated and must be inferred from the text content, historical records, and codicological cl
ues. Given a transcribed text, machine learning has successfully been used to estimate the year of production. In this paper, we present an overview of several estimation approaches for historical text archives spanning from the 12th century until today.
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
