Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAre we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content
هل نحن بشر أو نحن المستخدمون؟دور معالجة اللغات الطبيعية في توصية الأخبار التي تركز على الإنسان أن يدفع المستخدمين إلى محتوى متنوع
342
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في ورقة الموقف هذه، نقدم جدول أعمال وأفكار بحثية لتسهيل التعرض للنقاط المتنوعة في توصية الأخبار. التوصية الأخبار من وجهات النظر المتنوعة مهمة لمنع تأثيرات فقاعة المرشح المحتملة في الاستهلاك الأخبار، وتحفيز نقاش صحي ديمقراطي. لحساب التعقيد الذي يتصاعد للبشر كمواطنين في الديمقراطية، نتوقع (من بين أمور أخرى) اختلافات على المستوى الفردي في قبول التنوع. نقوم بتوصيل هذه الفكرة بالتقنيات في معالجة اللغة الطبيعية، حيث تتيح لنا نماذج اللغة التوزيعية لوضع مستخدمين مختلفين ومقالات إخبارية في مساحة متعددة الأبعاد بناء على المحتوى الدلالي، حيث يتم تشغيل التنوع كمسافة وتباين. وبهذه الطريقة، يمكننا تصميم خطوط العرض الفردية من التنوع "بالنسبة للمستخدمين المختلفين، وبالتالي تخصيص تنوع وجهة نظر لدعم مناقشة عامة صحية. بالإضافة إلى ذلك، نحدد القضايا التقنية والأخلاقية والمفاهيمية المتعلقة بأفكارنا المقدمة. وصفنا يصف كيف يمكن ل NLP لعب دورا رئيسيا في تنويع توصيات الأخبار.
In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation. Recommending news from diverse viewpoints is important to prevent potential filter bubble effects in news consumption, and stimulate a healthy democratic debate.To account for the complexity that is inherent to humans as citizens in a democracy, we anticipate (among others) individual-level differences in acceptance of diversity. We connect this idea to techniques in Natural Language Processing, where distributional language models would allow us to place different users and news articles in a multidimensional space based on semantic content, where diversity is operationalized as distance and variance. In this way, we can model individual latitudes of diversity'' for different users, and thus personalize viewpoint diversity in support of a healthy public debate. In addition, we identify technical, ethical and conceptual issues related to our presented ideas. Our investigation describes how NLP can play a central role in diversifying news recommendations.
References used
https://aclanthology.org/
rate research
Read More
Discourse parsers recognize the intentional and inferential relationships that organize extended texts. They have had a great influence on a variety of NLP tasks as well as theoretical studies in linguistics and cognitive science. However it is often
difficult to achieve good results from current discourse models, largely due to the difficulty of the task, particularly recognizing implicit discourse relations. Recent developments in transformer-based models have shown great promise on these analyses, but challenges still remain. We present a position paper which provides a systematic analysis of the state of the art discourse parsers. We aim to examine the performance of current discourse parsing models via gradual domain shift: within the corpus, on in-domain texts, and on out-of-domain texts, and discuss the differences between the transformer-based models and the previous models in predicting different types of implicit relations both inter- and intra-sentential. We conclude by describing several shortcomings of the existing models and a discussion of how future work should approach this problem.
Dialogue summarization is a long-standing task in the field of NLP, and several data sets with dialogues and associated human-written summaries of different styles exist. However, it is unclear for which type of dialogue which type of summary is most
appropriate. For this reason, we apply a linguistic model of dialogue types to derive matching summary items and NLP tasks. This allows us to map existing dialogue summarization data sets into this model and identify gaps and potential directions for future work. As part of this process, we also provide an extensive overview of existing dialogue summarization data sets.
Recent Deep Learning (DL) summarization models greatly outperform traditional summarization methodologies, generating high-quality summaries. Despite their success, there are still important open issues, such as the limited engagement and trust of us
ers in the whole process. In order to overcome these issues, we reconsider the task of summarization from a human-centered perspective. We propose to integrate a user interface with an underlying DL model, instead of tackling summarization as an isolated task from the end user. We present a novel system, where the user can actively participate in the whole summarization process. We also enable the user to gather insights into the causative factors that drive the model's behavior, exploiting the self-attention mechanism. We focus on the financial domain, in order to demonstrate the efficiency of generic DL models for domain-specific applications. Our work takes a first step towards a model-interface co-design approach, where DL models evolve along user needs, paving the way towards human-computer text summarization interfaces.
Natural Language Processing (NLP) systems are at the heart of many critical automated decision-making systems making crucial recommendations about our future world. Gender bias in NLP has been well studied in English, but has been less studied in oth
er languages. In this paper, a team including speakers of 9 languages - Chinese, Spanish, English, Arabic, German, French, Farsi, Urdu, and Wolof - reports and analyzes measurements of gender bias in the Wikipedia corpora for these 9 languages. We develop extensions to profession-level and corpus-level gender bias metric calculations originally designed for English and apply them to 8 other languages, including languages that have grammatically gendered nouns including different feminine, masculine, and neuter profession words. We discuss future work that would benefit immensely from a computational linguistics perspective.
Statistical approaches to processing natural language text have become dominant in recent years. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations.
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
