Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCrowdsourcing Natural Language Data at Scale: A Hands-On Tutorial
Growdsourcing بيانات اللغة الطبيعية على نطاق الحجم: تعليمي
386
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في هذا البرنامج التعليمي، نقدم جزءا من الخبرة الصناعية الفريدة في التعليق التوضيحي لبيانات اللغة الطبيعية فعالة عبر الجماعة الجماعية المشتركة من قبل كل من الباحثين والمهندسين الرئيسيين من ياندكس.سنقوم بإعطاء مقدمة لوضع علامات البيانات عبر أسواق الجماعة الجماعية العامة وستقدم المكونات الرئيسية لجمع الملصقات الفعالة.سيتبع ذلك جلسة عملية، حيث يتناول المشاركون مهمة إنتاج موارد عالمية حقيقية، تجربة مع تحديد إعدادات عملية وضع العلامات، وإطلاق مشروع مجموعة الملصقات الخاصة بهم على أحد أكبر أسواق الجماعة الجماعية.سيتم تشغيل المشروعات على الحشود الحقيقية داخل جلسة البرنامج التعليمي وسنقدم تقنيات مفيدة لمراقبة الجودة وتزويد الحضور بفرصة لمناقشة أفكارهم التوضيحية الخاصة بهم.
In this tutorial, we present a portion of unique industry experience in efficient natural language data annotation via crowdsourcing shared by both leading researchers and engineers from Yandex. We will make an introduction to data labeling via public crowdsourcing marketplaces and will present the key components of efficient label collection. This will be followed by a practical session, where participants address a real-world language resource production task, experiment with selecting settings for the labeling process, and launch their label collection project on one of the largest crowdsourcing marketplaces. The projects will be run on real crowds within the tutorial session and we will present useful quality control techniques and provide the attendees with an opportunity to discuss their own annotation ideas.
References used
https://aclanthology.org/
rate research
Read More
The advent of Deep Learning and the availability of large scale datasets has accelerated research on Natural Language Generation with a focus on newer tasks and better models. With such rapid progress, it is vital to assess the extent of scientific p
rogress made and identify the areas/components that need improvement. To accomplish this in an automatic and reliable manner, the NLP community has actively pursued the development of automatic evaluation metrics. Especially in the last few years, there has been an increasing focus on evaluation metrics, with several criticisms of existing metrics and proposals for several new metrics. This tutorial presents the evolution of automatic evaluation metrics to their current state along with the emerging trends in this field by specifically addressing the following questions: (i) What makes NLG evaluation challenging? (ii) Why do we need automatic evaluation metrics? (iii) What are the existing automatic evaluation metrics and how can they be organised in a coherent taxonomy? (iv) What are the criticisms and shortcomings of existing metrics? (v) What are the possible future directions of research?
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.
Reviewing contracts is a time-consuming procedure that incurs large expenses to companies and social inequality to those who cannot afford it. In this work, we propose document-level natural language inference (NLI) for contracts'', a novel, real-wor
ld application of NLI that addresses such problems. In this task, a system is given a set of hypotheses (such as Some obligations of Agreement may survive termination.'') and a contract, and it is asked to classify whether each hypothesis is entailed by'', contradicting to'' or not mentioned by'' (neutral to) the contract as well as identifying evidence'' for the decision as spans in the contract. We annotated and release the largest corpus to date consisting of 607 annotated contracts. We then show that existing models fail badly on our task and introduce a strong baseline, which (a) models evidence identification as multi-label classification over spans instead of trying to predict start and end tokens, and (b) employs more sophisticated context segmentation for dealing with long documents. We also show that linguistic characteristics of contracts, such as negations by exceptions, are contributing to the difficulty of this task and that there is much room for improvement.
It is generally agreed upon in the natural language processing (NLP) community that ethics should be integrated into any curriculum. Being aware of and understanding the relevant core concepts is a prerequisite for following and participating in the
discourse on ethical NLP. We here present ready-made teaching material in the form of slides and practical exercises on ethical issues in NLP, which is primarily intended to be integrated into introductory NLP or computational linguistics courses. By making this material freely available, we aim at lowering the threshold to adding ethics to the curriculum. We hope that increased awareness will enable students to identify potentially unethical behavior.
Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding of a user request. We propose a scalable and automatic approach for im
proving NLU in a large-scale conversational AI system by leveraging implicit user feedback, with an insight that user interaction data and dialog context have rich information embedded from which user satisfaction and intention can be inferred. In particular, we propose a domain-agnostic framework for curating new supervision data for improving NLU from live production traffic. With an extensive set of experiments, we show the results of applying the framework and improving NLU for a large-scale production system across 10 domains.
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
