Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSpellchecking for Children in Web Search: a Natural Language Interface Case-study
SpellChecking للأطفال في البحث على شبكة الإنترنت: دراسة حالات واجهة اللغة الطبيعية
485
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
بالنظر إلى الطبيعة الأكثر انتشارا لواجهات اللغة الطبيعية، من المهم بشكل متزايد فهم من يصل إلى هذه الواجهات، وكيف يتم استخدام هذه الواجهات.في هذه الورقة، نستكشف التدقيق الإملائي في سياق البحث على شبكة الإنترنت مع الأطفال كجمهور مستهدف.على وجه الخصوص، عبر مراجعة الأدب، نظرا لأنه، بينما تستخدم أدوات البحث الشعبية على نطاق واسع للأطفال.ثم نستخدم إذكاء التدقيق الإملائي كدراسة للحالة لتسليط الضوء على الحاجة إلى نهج متعدد التخصصات يجمع معالجة اللغات الطبيعية والتعليم والتفاعل بين الإنسان للكهرباء لمعالجة مشكلة استرجاع المعلومات المعروفة: الاستعلام عن خطأ.نستنتج أنه من الضروري أن يكون لهؤلاء الذين تم تصميم الواجهات التي تم تصميم الواجهات صوتا في عملية التصميم.
Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are accessing those interfaces, and how those interfaces are being used. In this paper, we explore spellchecking in the context of web search with children as the target audience. In particular, via a literature review we show that, while widely used, popular search tools are ill-designed for children. We then use spellcheckers as a case study to highlight the need for an interdisciplinary approach that brings together natural language processing, education, human-computer interaction to address a known information retrieval problem: query misspelling. We conclude that it is imperative that those for whom the interfaces are designed have a voice in the design process.
References used
https://aclanthology.org/
rate research
Read More
AI assistants can now carry out tasks for users by directly interacting with website UIs. Current semantic parsing and slot-filling techniques cannot flexibly adapt to many different websites without being constantly re-trained. We propose FLIN, a na
tural language interface for web navigation that maps user commands to concept-level actions (rather than low-level UI actions), thus being able to flexibly adapt to different websites and handle their transient nature. We frame this as a ranking problem: given a user command and a webpage, FLIN learns to score the most relevant navigation instruction (involving action and parameter values). To train and evaluate FLIN, we collect a dataset using nine popular websites from three domains. Our results show that FLIN was able to adapt to new websites in a given domain.
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.
Although general question answering has been well explored in recent years, temporal question answering is a task which has not received as much focus. Our work aims to leverage a popular approach used for general question answering, answer extractio
n, in order to find answers to temporal questions within a paragraph. To train our model, we propose a new dataset, inspired by SQuAD, a state-of-the-art question answering corpus, specifically tailored to provide rich temporal information by adapting the corpus WikiWars, which contains several documents on history's greatest conflicts. Our evaluation shows that a pattern matching deep learning model, often used in general question answering, can be adapted to temporal question answering, if we accept to ask questions whose answers must be directly present within a text.
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.
We propose an approach to automatically test for originality in generation tasks where no standard automatic measures exist. Our proposal addresses original uses of language, not necessarily original ideas. We provide an algorithm for our approach an
d a run-time analysis. The algorithm, which finds all of the original fragments in a ground-truth corpus and can reveal whether a generated fragment copies an original without attribution, has a run-time complexity of theta(nlogn) where n is the number of sentences in the ground truth.
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
