Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userIdiosyncratic but not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive yet Consistent Individual Styles
الخصوصيات ولكن ليس تعسفي: التعلم idiolects في سجلات عبر الإنترنت يكشف عن أنماط فردية مميزة ولكنها ثابتة
80
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
غالبا ما يكون اختلاف الفرد في أسلوب الكتابة وظيفة من السمات الاجتماعية والشخصية. في حين أن التباين الاجتماعي المنظم قد درس على نطاق واسع، مثل التباين القائم على النوع الاجتماعي، فإن أقل بكثير معروف حول كيفية وصف الأساليب الفردية بسبب طبيعتها الخصوصية. نقدم نهجا جديدا لدراسة idiolects من خلال مقارنة هائلة للمؤلف عبر المؤلف لتحديد وترميز الميزات الأسلوبية. يحقق النموذج العصبي الأداء القوي في تحديد التأليف على النصوص القصيرة ومن خلال مهمة التحقيق القائم على التشبيه، يظهر أن التمثيلات المستفادة تظهر منتديات مفاجئة ترميز التحولات النوعية والكمية من الأساليب القطرية. من خلال اضطراب النص، نحدد المساهمات النسبية للعناصر اللغوية المختلفة على التباين الاضطراب. علاوة على ذلك، فإننا نقدم وصفا ل idiolects من خلال قياس الاختلاف بين المؤلفين و interra، مما يدل على أن الاختلاف في idiolects غالبا ما يكون مميزا بعد متسقة.
An individual's variation in writing style is often a function of both social and personal attributes. While structured social variation has been extensively studied, e.g., gender based variation, far less is known about how to characterize individual styles due to their idiosyncratic nature. We introduce a new approach to studying idiolects through a massive cross-author comparison to identify and encode stylistic features. The neural model achieves strong performance at authorship identification on short texts and through an analogy-based probing task, showing that the learned representations exhibit surprising regularities that encode qualitative and quantitative shifts of idiolectal styles. Through text perturbation, we quantify the relative contributions of different linguistic elements to idiolectal variation. Furthermore, we provide a description of idiolects through measuring inter- and intra-author variation, showing that variation in idiolects is often distinctive yet consistent.
References used
https://aclanthology.org/
rate research
Read More
In recent years, world business in online discussions and opinion sharing on social media is booming. Re-entry prediction task is thus proposed to help people keep track of the discussions which they wish to continue. Nevertheless, existing works onl
y focus on exploiting chatting history and context information, and ignore the potential useful learning signals underlying conversation data, such as conversation thread patterns and repeated engagement of target users, which help better understand the behavior of target users in conversations. In this paper, we propose three interesting and well-founded auxiliary tasks, namely, Spread Pattern, Repeated Target user, and Turn Authorship, as the self-supervised signals for re-entry prediction. These auxiliary tasks are trained together with the main task in a multi-task manner. Experimental results on two datasets newly collected from Twitter and Reddit show that our method outperforms the previous state-of-the-arts with fewer parameters and faster convergence. Extensive experiments and analysis show the effectiveness of our proposed models and also point out some key ideas in designing self-supervised tasks.
Adaptive Machine Translation purports to dynamically include user feedback to improve translation quality. In a post-editing scenario, user corrections of machine translation output are thus continuously incorporated into translation models, reducing
or eliminating repetitive error editing and increasing the usefulness of automated translation. In neural machine translation, this goal may be achieved via online learning approaches, where network parameters are updated based on each new sample. This type of adaptation typically requires higher learning rates, which can affect the quality of the models over time. Alternatively, less aggressive online learning setups may preserve model stability, at the cost of reduced adaptation to user-generated corrections. In this work, we evaluate different online learning configurations over time, measuring their impact on user-generated samples, as well as separate in-domain and out-of-domain datasets. Results in two different domains indicate that mixed approaches combining online learning with periodic batch fine-tuning might be needed to balance the benefits of online learning with model stability.
We use dialogue act recognition (DAR) to investigate how well BERT represents utterances in dialogue, and how fine-tuning and large-scale pre-training contribute to its performance. We find that while both the standard BERT pre-training and pretraini
ng on dialogue-like data are useful, task-specific fine-tuning is essential for good performance.
Online platforms and communities establish their own norms that govern what behavior is acceptable within the community. Substantial effort in NLP has focused on identifying unacceptable behaviors and, recently, on forecasting them before they occur.
However, these efforts have largely focused on toxicity as the sole form of community norm violation. Such focus has overlooked the much larger set of rules that moderators enforce. Here, we introduce a new dataset focusing on a more complete spectrum of community norms and their violations in the local conversational and global community contexts. We introduce a series of models that use this data to develop context- and community-sensitive norm violation detection, showing that these changes give high performance.
People utilize online forums to either look for information or to contribute it. Because of their growing popularity, certain online forums have been created specifically to provide support, assistance, and opinions for people suffering from mental i
llness. Depression is one of the most frequent psychological illnesses worldwide. People communicate more with online forums to find answers for their psychological disease. However, there is no mechanism to measure the severity of depression in each post and give higher importance to those who are diagnosed more severely depressed. Despite the fact that numerous researches based on online forum data and the identification of depression have been conducted, the severity of depression is rarely explored. In addition, the absence of datasets will stymie the development of novel diagnostic procedures for practitioners. From this study, we offer a dataset to support research on depression severity evaluation. The computational approach to measure an automatic process, identified severity of depression here is quite novel approach. Nonetheless, this elaborate measuring severity of depression in online forum posts is needed to ensure the measurement scales used in our research meets the expected norms of scientific research.
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
