Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe Impact of Answers in Referential Visual Dialog
تأثير الإجابات في الحوار المرجعية المرجعية
107
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في مهمة الحوار المرئي تخمين؟!يحتفظ لاعبان بتاريخ حوار لتحديد كائن سري في صورة.competionally، يتم تصميم هذا باستخدام وحدة توليد سؤال ووحدة تخييس لدور السائل ونموذج الرد على الأوراكل، والرد على الأسئلة التي تم إنشاؤها.هذا يثير سؤال: ما هو خطر وجود نموذج Oracle غير كامل؟نحن هنا نقدم العمل في إحراز تقدم في دراسة تأثير نماذج الإجابة المختلفة في أسئلة البشرية التي تم إنشاؤها في التخمين ؟!..نظهر أن الوصول إلى إجابات عالية الجودة له تأثير مباشر على مهمة التخمين للحوار البشري وتجادل أن الإجابات الأفضل يمكن أن تساعد في تدريب نماذج توليد الأسئلة الأفضل.
In the visual dialog task GuessWhat?! two players maintain a dialog in order to identify a secret object in an image. Computationally, this is modeled using a question generation module and a guesser module for the questioner role and an answering model, the Oracle, to answer the generated questions. This raises a question: what's the risk of having an imperfect oracle model?. Here we present work in progress in the study of the impact of different answering models in human generated questions in GuessWhat?!. We show that having access to better quality answers has a direct impact on the guessing task for human dialog and argue that better answers could help train better question generation models.
References used
https://aclanthology.org/
rate research
Read More
يتناول هذا البحث مصطلح ( المرجعية ) ، فهو مصطلح جديد، ليس له وجود بهذه الصيغة
في القرآن الكريم أو كتب التراث الإسلامي. إلا أن معناه و مضمونه يتصل بنسب متين إلى
القرآن الكريم، و كتب التراث الإسلامي، و لكن في غير لفظ : ( المرجعية ) .
و تحاول هذه الد
راسة أن تتابع هذا المفهوم في القرآن الكريم من خلال المصطلحات المتعددة
التي تعبر عنه ، و ترصد ما يتعلق به من شروط و قيود، و تجعل منه نظرية متكاملة ، و ذلك
باستقراء المواضع التي جاء بها مفهوم المرجعية في القرآن الكريم، سواء في تحديد
المفهوم، أو شروط من يتصف به، أو النماذج التي قدمها القرآن الكريم مراجع للناس.
كما يسلك البحث إضافة إلى الاستقراء منهج التحليل، فيقوم بتحليل النصوص القرآنية
و شروحها المنقولة عن كبار علماء التفسير ؛ للتوصل إلى الرؤية القرآنية المتكاملة لهذا
المصطلح .
The research aims mainly to study the method of Benchmarking as a mean for continuous improvement of quality and the possibility of its usage in the Syrian banks, and to figure out any obstacles for such application therefore finding the right solutions.
Modern deep learning models for natural language processing rely heavily on large amounts of annotated texts. However, obtaining such texts may be difficult when they contain personal or confidential information, for example, in health or legal domai
ns. In this work, we propose a method of de-identifying free-form text documents by carefully redacting sensitive data in them. We show that our method preserves data utility for text classification, sequence labeling and question answering tasks.
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context. In this paper, we study how to address two fundamental challenges for this task: (1) reasoning over underlying semantic st
ructures among dialog rounds and (2) identifying several appropriate answers to the given question. To address these challenges, we propose a Sparse Graph Learning (SGL) method to formulate visual dialog as a graph structure learning task. SGL infers inherently sparse dialog structures by incorporating binary and score edges and leveraging a new structural loss function. Next, we introduce a Knowledge Transfer (KT) method that extracts the answer predictions from the teacher model and uses them as pseudo labels. We propose KT to remedy the shortcomings of single ground-truth labels, which severely limit the ability of a model to obtain multiple reasonable answers. As a result, our proposed model significantly improves reasoning capability compared to baseline methods and outperforms the state-of-the-art approaches on the VisDial v1.0 dataset. The source code is available at https://github.com/gicheonkang/SGLKT-VisDial.
Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains. It has been recently shown that the probabilities given by a large, multilingual
model can achieve state of the art results when used as a reference-free metric. We experiment with various modifications to this model, and demonstrate that by scaling it up we can match the performance of BLEU. We analyze various potential weaknesses of the approach, and find that it is surprisingly robust and likely to offer reasonable performance across a broad spectrum of domains and different system qualities.
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
