Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCome hither or go away? Recognising pre-electoral coalition signals in the news
تعال إلى هنا أو اذهب بعيدا؟الاعتراف بإشارات الائتلاف قبل الانتخابات في الأخبار
145
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في هذه الورقة، نقدم مهمة تنبؤ إشارة التحالف السياسي من النص، أي مهمة الاعتراف عن التغطية الإخبارية التي سبقت الانتخابات (الأمم المتحدة) استعداد الأحزاب السياسية لتشكيل ائتلاف حكومي.نحل مشكلتنا إلى مهمتين ذي صلة، ولكنها متبقية: (1) توقع ما إذا كان البيان المبلغ عنه من سياسي أو صحفي يشير إلى تحالف محتمل و (2) يتوقع قطبية الإشارة - أي ما إذا كان المتكلم فيصالح أو ضد الائتلاف.بالنسبة لهذا، نستكشف فوائد التعلم المتعدد المهام والتحقيق في ما هو الأنسب من الإعداد وصياغة المهمة لكل مهمة فرعية.نقيم نهجنا، بناء على مقالات جريدة مشفرة باليد، تغطي الانتخابات في ثلاث دول (أيرلندا وألمانيا والنمسا) ولغتين (الإنجليزية والألمانية).تظهر نتائجنا أن نهج التعلم متعدد المهام يمكن أن يؤدي إلى تحسين النتائج على خط أساسي قوي في مجال تحويل التحويل الأحادي.
In this paper, we introduce the task of political coalition signal prediction from text, that is, the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a government coalition. We decompose our problem into two related, but distinct tasks: (i) predicting whether a reported statement from a politician or a journalist refers to a potential coalition and (ii) predicting the polarity of the signal -- namely, whether the speaker is in favour of or against the coalition. For this, we explore the benefits of multi-task learning and investigate which setup and task formulation is best suited for each sub-task. We evaluate our approach, based on hand-coded newspaper articles, covering elections in three countries (Ireland, Germany, Austria) and two languages (English, German). Our results show that the multi-task learning approach can further improve results over a strong monolingual transfer learning baseline.
References used
https://aclanthology.org/
rate research
Read More
Recent work has demonstrated that pre-training in-domain language models can boost performance when adapting to a new domain. However, the costs associated with pre-training raise an important question: given a fixed budget, what steps should an NLP
practitioner take to maximize performance? In this paper, we study domain adaptation under budget constraints, and approach it as a customer choice problem between data annotation and pre-training. Specifically, we measure the annotation cost of three procedural text datasets and the pre-training cost of three in-domain language models. Then we evaluate the utility of different combinations of pre-training and data annotation under varying budget constraints to assess which combination strategy works best. We find that, for small budgets, spending all funds on annotation leads to the best performance; once the budget becomes large enough, a combination of data annotation and in-domain pre-training works more optimally. We therefore suggest that task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain.
Today, news media organizations regularly engage with readers by enabling them to comment on news articles. This creates the need for comment moderation and removal of disallowed comments -- a time-consuming task often performed by human moderators.
In this paper we approach the problem of automatic news comment moderation as classification of comments into blocked and not blocked categories. We construct a novel dataset of annotated English comments, experiment with cross-lingual transfer of comment labels and evaluate several machine learning models on datasets of Croatian and Estonian news comments. Team name: SuperAdmin; Challenge: Detection of blocked comments; Tools/models: CroSloEn BERT, FinEst BERT, 24Sata comment dataset, Ekspress comment dataset.
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 consumpti
on, 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.
Despite constant improvements in machine translation quality, automatic poetry translation remains a challenging problem due to the lack of open-sourced parallel poetic corpora, and to the intrinsic complexities involved in preserving the semantics,
style and figurative nature of poetry. We present an empirical investigation for poetry translation along several dimensions: 1) size and style of training data (poetic vs. non-poetic), including a zero-shot setup; 2) bilingual vs. multilingual learning; and 3) language-family-specific models vs. mixed-language-family models. To accomplish this, we contribute a parallel dataset of poetry translations for several language pairs. Our results show that multilingual fine-tuning on poetic text significantly outperforms multilingual fine-tuning on non-poetic text that is 35X larger in size, both in terms of automatic metrics (BLEU, BERTScore, COMET) and human evaluation metrics such as faithfulness (meaning and poetic style). Moreover, multilingual fine-tuning on poetic data outperforms bilingual fine-tuning on poetic data.
We submitted two uni-directional models, one for English→Icelandic direction and other for Icelandic→English direction. Our news translation system is based on the transformer-big architecture, it makes use of corpora filtering, back-translation and
forward translation applied to parallel and monolingual data alike
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
