Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRobustness and Adversarial Examples in Natural Language Processing
أمثلة متمردة وممدرية في معالجة اللغة الطبيعية
440
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تشير الدراسات الحديثة إلى أن العديد من أنظمة NLP حساسة وعرضة للاضطرابات الصغيرة للمدخلات ولا تعميمها بشكل جيد عبر مجموعات البيانات المختلفة. هذا الافتقار إلى المتانة ينطبق على استخدام أنظمة NLP في تطبيقات العالم الحقيقي. يهدف هذا البرنامج التعليمي إلى زيادة الوعي بالشواغل العملية حول متانة NLP. يستهدف الباحثون والممارسون الخماسيون الذين يهتمون ببناء أنظمة NLP موثوقة. على وجه الخصوص، سنراجع الدراسات الحديثة حول تحليل ضعف أنظمة NLP عند مواجهة المدخلات والبيانات المعديين مع تحول التوزيع. سوف نقدم للجمهور بهدف شامل من 1) كيفية استخدام أمثلة الخصومة لفحص ضعف نماذج NLP وتسهيل تصحيح الأخطاء؛ 2) كيفية تعزيز متانة نماذج NLP الحالية والدفاع ضد المدخلات الخصومة؛ 3) كيف يؤثر النظر في المتانة على تطبيقات NLP العالمية الحقيقية المستخدمة في حياتنا اليومية. سنختتم البرنامج التعليمي عن طريق تحديد اتجاهات البحث في المستقبل في هذا المجال.
Recent studies show that many NLP systems are sensitive and vulnerable to a small perturbation of inputs and do not generalize well across different datasets. This lack of robustness derails the use of NLP systems in real-world applications. This tutorial aims at bringing awareness of practical concerns about NLP robustness. It targets NLP researchers and practitioners who are interested in building reliable NLP systems. In particular, we will review recent studies on analyzing the weakness of NLP systems when facing adversarial inputs and data with a distribution shift. We will provide the audience with a holistic view of 1) how to use adversarial examples to examine the weakness of NLP models and facilitate debugging; 2) how to enhance the robustness of existing NLP models and defense against adversarial inputs; and 3) how the consideration of robustness affects the real-world NLP applications used in our daily lives. We will conclude the tutorial by outlining future research directions in this area.
References used
https://aclanthology.org/
rate research
Read More
This tutorial surveys the latest technical progress of syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks, in which semantic role labeling (SRL) and machine translation (MT) are the representative NLP tasks
that have always been beneficial from informative syntactic clues since a long time ago, though the advance from end-to-end deep learning models shows new results. In this tutorial, we will first introduce the background and the latest progress of syntactic parsing and SRL/NMT. Then, we will summarize the key evidence about the syntactic impacts over these two concerning tasks, and explore the behind reasons from both computational and linguistic backgrounds.
This article explores the potential for Natural Language Processing (NLP) to enable a more effective, prevention focused and less confrontational policing model that has hitherto been too resource consuming to implement at scale. Problem-Oriented Pol
icing (POP) is a potential replacement, at least in part, for traditional policing which adopts a reactive approach, relying heavily on the criminal justice system. By contrast, POP seeks to prevent crime by manipulating the underlying conditions that allow crimes to be committed. Identifying these underlying conditions requires a detailed understanding of crime events - tacit knowledge that is often held by police officers but which can be challenging to derive from structured police data. One potential source of insight exists in unstructured free text data commonly collected by police for the purposes of investigation or administration. Yet police agencies do not typically have the skills or resources to analyse these data at scale. In this article we argue that NLP offers the potential to unlock these unstructured data and by doing so allow police to implement more POP initiatives. However we caution that using NLP models without adequate knowledge may either allow or perpetuate bias within the data potentially leading to unfavourable outcomes.
There are thousands of papers about natural language processing and computational linguistics, but very few textbooks. I describe the motivation and process for writing a college textbook on natural language processing, and offer advice and encouragement for readers who may be interested in writing a textbook of their own.
In this paper, we propose a definition and taxonomy of various types of non-standard textual content -- generally referred to as noise'' -- in Natural Language Processing (NLP). While data pre-processing is undoubtedly important in NLP, especially wh
en dealing with user-generated content, a broader understanding of different sources of noise and how to deal with them is an aspect that has been largely neglected. We provide a comprehensive list of potential sources of noise, categorise and describe them, and show the impact of a subset of standard pre-processing strategies on different tasks. Our main goal is to raise awareness of non-standard content -- which should not always be considered as noise'' -- and of the need for careful, task-dependent pre-processing. This is an alternative to blanket, all-encompassing solutions generally applied by researchers through standard'' pre-processing pipelines. The intention is for this categorisation to serve as a point of reference to support NLP researchers in devising strategies to clean, normalise or embrace non-standard content.
How can we design Natural Language Processing (NLP) systems that learn from human feedback? There is a growing research body of Human-in-the-loop (HITL) NLP frameworks that continuously integrate human feedback to improve the model itself. HITL NLP r
esearch is nascent but multifarious---solving various NLP problems, collecting diverse feedback from different people, and applying different methods to learn from human feedback. We present a survey of HITL NLP work from both Machine Learning (ML) and Human-computer Interaction (HCI) communities that highlights its short yet inspiring history, and thoroughly summarize recent frameworks focusing on their tasks, goals, human interactions, and feedback learning methods. Finally, we discuss future studies for integrating human feedback in the NLP development loop.
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
