Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFLIN: A Flexible Natural Language Interface for Web Navigation
Flin: واجهة لغة طبيعية مرنة للملاحة على شبكة الإنترنت
394
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يمكن لمساعدات منظمة العفو الدولية الآن تنفيذ مهام للمستخدمين بالتفاعل بشكل مباشر مع موقع UIS بشكل مباشر.لا يمكن أن تتكيف تقنيات التحليل الدلالي الحالية والفتحية بمرونة مع العديد من مواقع الويب المختلفة دون إعادة تدريبها باستمرار.نقترح Flin، وهي واجهة لغة طبيعية للملاحة على شبكة الإنترنت التي تقوم بتقدم أوامر المستخدم إلى إجراءات المستوى المفاهيم (بدلا من إجراءات UI ذات المستوى المنخفض)، وبالتالي القدرة على التكيف بمرونة مع مواقع الويب المختلفة والتعامل مع طبيعتها العابرة.نحن نؤيد ذلك كمشكلة في الترتيب: مع إعطاء أمر مستخدم وشباج ويب، يتعلم Flin تسجيل تعليمات الملاحة الأكثر صلة (تنطوي على قيم العمل والمعلمات).لتدريب وتقييم Flin، نجمع مجموعة بيانات باستخدام تسعة مواقع مشهورة من ثلاثة مجالات.تظهر نتائجنا أن Flin تمكنت من التكيف مع مواقع الويب الجديدة في مجال معين.
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 natural 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.
References used
https://aclanthology.org/
rate research
Read More
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 sea
rch 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.
In the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions. A challenge in this task is how to handle off the path' scenarios where an agent veers from a reference path. Prio
r work supervises the agent with actions based on the shortest path from the agent's location to the goal, but such goal-oriented supervision is often not in alignment with the instruction. Furthermore, the evaluation metrics employed by prior work do not measure how much of a language instruction the agent is able to follow. In this work, we propose a simple and effective language-aligned supervision scheme, and a new metric that measures the number of sub-instructions the agent has completed during navigation.
Subtle and overt racism is still present both in physical and online communities today and has impacted many lives in different segments of the society. In this short piece of work, we present how we're tackling this societal issue with Natural Langu
age Processing. We are releasing BiasCorp, a dataset containing 139,090 comments and news segment from three specific sources - Fox News, BreitbartNews and YouTube. The first batch (45,000 manually annotated) is ready for publication. We are currently in the final phase of manually labeling the remaining dataset using Amazon Mechanical Turk. BERT has been used widely in several downstream tasks. In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer. hBert generalizes well across different distributions with the added advantage of a reduced model complexity. We are also releasing a JavaScript library 3 and a Chrome Extension Application, to help developers make use of our trained model in web applications (say chat application) and for users to identify and report racially biased contents on the web respectively
In the pandemic period, the stay-at-home trend forced businesses to switch their activities to digital mode, for example, app-based payment methods, social distancing via social media platforms, and other digital means have become an integral part of
our lives. Sentiment analysis of textual information in user comments is a topical task in emotion AI because user comments or reviews are not homogeneous, they contain sparse context behind, and are misleading both for human and computer. Barriers arise from the emotional language enriched with slang, peculiar spelling, transliteration, use of emoji and their symbolic counterparts, and code-switching. For low resource languages sentiment analysis has not been worked upon extensively, because of an absence of ready-made tools and linguistic resources for sentiment analysis. This research focuses on developing a method for aspect-based sentiment analysis for Kazakh-language reviews in Android Google Play Market.
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
