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ReadTwice: قراءة مستندات كبيرة جدا مع ذكريات
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غالبا ما تتطلب المهام المكثفة المعرفة مثل الإجابة على الأسئلة استيعاب معلومات من أقسام مختلفة من المدخلات الكبيرة مثل الكتب أو مجموعات المقالات.نقترح ReadTwice، وهي تقنية بسيطة وفعالة تجمع بين العديد من نقاط القوة من الأساليب السابقة لنموذج التبعيات الطويلة المدى مع المحولات.الفكرة الرئيسية هي قراءة النص في شرائح صغيرة، بالتوازي، تلخيص كل قطعة في جدول الذاكرة لاستخدامه في القراءة الثانية للنص.نظهر أن الطريقة تتفوق على نماذج من حجم قابلة للمقارنة على العديد من مجموعات بيانات الإجابة على الأسئلة (QA) وتعيين حالة جديدة من الفن على المهمة السرقة الصعبة، مع أسئلة حول الكتب بأكملها.
Knowledge-intensive tasks such as question answering often require assimilating information from different sections of large inputs such as books or article collections. We propose ReadTwice, a simple and effective technique that combines several strengths of prior approaches to model long-range dependencies with Transformers. The main idea is to read text in small segments, in parallel, summarizing each segment into a memory table to be used in a second read of the text. We show that the method outperforms models of comparable size on several question answering (QA) datasets and sets a new state of the art on the challenging NarrativeQA task, with questions about entire books.
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Feed-forward layers constitute two-thirds of a transformer model's parameters, yet their role in the network remains under-explored. We show that feed-forward layers in transformer-based language models operate as key-value memories, where each key c
orrelates with textual patterns in the training examples, and each value induces a distribution over the output vocabulary. Our experiments show that the learned patterns are human-interpretable, and that lower layers tend to capture shallow patterns, while upper layers learn more semantic ones. The values complement the keys' input patterns by inducing output distributions that concentrate probability mass on tokens likely to appear immediately after each pattern, particularly in the upper layers. Finally, we demonstrate that the output of a feed-forward layer is a composition of its memories, which is subsequently refined throughout the model's layers via residual connections to produce the final output distribution.
A Photonic Crystal Fiber (PCF) is a special class of optical fibers which is made of a single material
and having air holes in the cladding. This paper studies and compares the optical characteristics such as
effective area, confinement loss and no
nlinearity, among three different PCF's structures: Hexagonal PCF (HPCF),
Octagonal PCF (O-PCF) and Decagonal PCF (D-PCF) with varied structural parameters (number of
the air-holes rings, the air-hole's diameter, and the lattice constant), and the target is to use the fiber in a
Raman amplifier. Proposed structures are simulated by using COMSOL MULTIPHYSICS which depends on
Finite Element Method (FEM).
The numerically simulated results shows that Decagonal PCF (D-PCF) offers lower confinement loss,
lower effective area, and larger value of nonlinearity than the other two structures. It is seen that Decagonal
PCF(D-PCF) is suitable for long transmission fiber applications.
We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as machine reading comprehension task based on a single given document
or passage. In this work, we aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. To facilitate such task, we introduce a new dataset that contains dialogues grounded in multiple documents from four different domains. We also explore modeling the dialogue-based and document-based contexts in the dataset. We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task.
Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications. To perform ABSA, the trained model is required to have a good understanding
of the contextual information, especially the particular patterns that suggest the sentiment polarity. However, these patterns typically vary in different sentences, especially when the sentences come from different sources (domains), which makes ABSA still very challenging. Although combining labeled data across different sources (domains) is a promising solution to address the challenge, in practical applications, these labeled data are usually stored at different locations and might be inaccessible to each other due to privacy or legal concerns (e.g., the data are owned by different companies). To address this issue and make the best use of all labeled data, we propose a novel ABSA model with federated learning (FL) adopted to overcome the data isolation limitations and incorporate topic memory (TM) proposed to take the cases of data from diverse sources (domains) into consideration. Particularly, TM aims to identify different isolated data sources due to data inaccessibility by providing useful categorical information for localized predictions. Experimental results on a simulated environment for FL with three nodes demonstrate the effectiveness of our approach, where TM-FL outperforms different baselines including some well-designed FL frameworks.
This study aims at showing the significance of this treaty as an
advanced document which expresses the Islamic ideals from various
aspects.
It points out especially the linguistic, stylistic, and technical aspects
of the topic under study. It als
o shows its characteristics and its position
among other treaties and documents. Furthermore, it clarifies the Islamic
forgiveness, justice, and respect to the believes of other people, and it
facilitates their reach to their faith, as well as their practice of their
worship.
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