Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDifferential Evaluation: a Qualitative Analysis of Natural Language Processing System Behavior Based Upon Data Resistance to Processing
التقييم التفاضلي: تحليل نوعي لسلوك نظام معالجة اللغة الطبيعي بناء على مقاومة البيانات للمعالجة
310
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
معظم الوقت، عند التعامل مع مهمة معينة لمعالجة اللغة الطبيعية، تتم مقارنة الأنظمة على أساس الإحصاءات العالمية مثل الاستدعاء والدقة ودرجة F1، وما إلى ذلك، بينما توفر هذه الدرجات فكرة عامة عن سلوك هذه الأنظمة، إنهم يتجاهلون جزءا رئيسيا من المعلومات التي يمكن أن تكون مفيدة لتقييم التقدم المحرز والتحديات المتبقية المتميزة: الصعوبة النسبية في حالات الاختبار. لمعالجة هذا القصور، نقدم فكرة التقييم التفاضلي الذي يحدد فعليا التقسيم العملي من الحالات في صناديق أكثر صعوبة تدريجيا من خلال الاستفادة من التنبؤات التي قدمتها مجموعة من الأنظمة. تمكننا مقارنة الأنظمة على طول صناديق الصعوبة هذه أن تنتجنا تحليلا مدققا لأسعارها النسبية، والتي نوضحها على حالتي الاستخدام: مقارنة بين النظم المشاركة في مهمة تصنيف النص متعدد الملصقات (CLF EHENGE 2018 ICD-10 ICD-10 )، ومقارنة النماذج العصبية المدربة للكشف عن الكيانات الطبية الحيوية (مجموعة بيانات علاقات الأمراض الكيميائية الثنائية الطبيوم الكيميائية).
Most of the time, when dealing with a particular Natural Language Processing task, systems are compared on the basis of global statistics such as recall, precision, F1-score, etc. While such scores provide a general idea of the behavior of these systems, they ignore a key piece of information that can be useful for assessing progress and discerning remaining challenges: the relative difficulty of test instances. To address this shortcoming, we introduce the notion of differential evaluation which effectively defines a pragmatic partition of instances into gradually more difficult bins by leveraging the predictions made by a set of systems. Comparing systems along these difficulty bins enables us to produce a finer-grained analysis of their relative merits, which we illustrate on two use-cases: a comparison of systems participating in a multi-label text classification task (CLEF eHealth 2018 ICD-10 coding), and a comparison of neural models trained for biomedical entity detection (BioCreative V chemical-disease relations dataset).
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.
The field of Natural Language Processing (NLP) changes rapidly, requiring course offerings to adjust with those changes, and NLP is not just for computer scientists; it's a field that should be accessible to anyone who has a sufficient background. In
this paper, I explain how students with Computer Science and Data Science backgrounds can be well-prepared for an upper-division NLP course at a large state university. The course covers probability and information theory, elementary linguistics, machine and deep learning, with an attempt to balance theoretical ideas and concepts with practical applications. I explain the course objectives, topics and assignments, reflect on adjustments to the course over the last four years, as well as feedback from students.
Despite its proven efficiency in other fields, data augmentation is less popular in the context of natural language processing (NLP) due to its complexity and limited results. A recent study (Longpre et al., 2020) showed for example that task-agnosti
c data augmentations fail to consistently boost the performance of pretrained transformers even in low data regimes. In this paper, we investigate whether data-driven augmentation scheduling and the integration of a wider set of transformations can lead to improved performance where fixed and limited policies were unsuccessful. Our results suggest that, while this approach can help the training process in some settings, the improvements are unsubstantial. This negative result is meant to help researchers better understand the limitations of data augmentation for NLP.
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.
In this paper we present a new Massive Open Online Course on Natural Language Processing, targeted at non-English speaking students. The course lasts 12 weeks, every week consists of lectures, practical sessions and quiz assigments. Three weeks out o
f 12 are followed by Kaggle-style coding assigments. Our course intents to serve multiple purposes: (i) familirize students with the core concepts and methods in NLP, such as language modelling or word or sentence representations, (ii) show that recent advances, including pre-trained Transformer-based models, are build upon these concepts; (iii) to introduce architectures for most most demanded real-life applications, (iii) to develop practical skills to process texts in multiple languages. The course was prepared and recorded during 2020 and so far have received positive feedback.
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
