Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFinding Pragmatic Differences Between Disciplines
العثور على الاختلافات العميمة بين التخصصات
243
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تحتوي الوثائق العلمية على درجة كبيرة من الاختلاف، سواء من حيث المحتوى (الدلالات) والهيكل (البراغماتية). تؤكد العمل المسبق في الوثيقة العلمية التفاهم على دلالات من خلال تلخيص المستندات ونمذجة موضوع Corpus ولكن تميل إلى حذف البراغماتية مثل تنظيم الوثائق وتدفقها. باستخدام مجموعة من الوثائق العلمية عبر 19 تخصصات وتقنيات النمذجة باللغة الحديثة، نتعلم مجموعة ثابتة من واصفات المجال الأذرع لمقاطع المستندات وإعادة التحديث "" The Corpus إلى هذه الواصفات (يشار إليها أيضا باسم "التطبيع" '). بعد ذلك، نقوم بتحليل موقف وطلب هذه الواصفات عبر المستندات لفهم العلاقة بين الانضباط والهيكل. نبلغ عن ضمنيات الأنماط الهيكلية داخل الانضباط، وتقليل التباينات، وبين الإصابة، ودعم الفرضية التي تشترك فيها المجتمعات العلمية، على الرغم من حجمها، وتنوعها، واتساعها، مشترك في طرق مماثلة للتعبير عن عملها. تضع نتائجنا الأساس للعمل في المستقبل في تقييم جودة البحث ونقل نمط المجال، وعمليات التحليل العملي.
Scholarly documents have a great degree of variation, both in terms of content (semantics) and structure (pragmatics). Prior work in scholarly document understanding emphasizes semantics through document summarization and corpus topic modeling but tends to omit pragmatics such as document organization and flow. Using a corpus of scholarly documents across 19 disciplines and state-of-the-art language modeling techniques, we learn a fixed set of domain-agnostic descriptors for document sections and retrofit'' the corpus to these descriptors (also referred to as normalization''). Then, we analyze the position and ordering of these descriptors across documents to understand the relationship between discipline and structure. We report within-discipline structural archetypes, variability, and between-discipline comparisons, supporting the hypothesis that scholarly communities, despite their size, diversity, and breadth, share similar avenues for expressing their work. Our findings lay the foundation for future work in assessing research quality, domain style transfer, and further pragmatic analysis.
References used
https://aclanthology.org/
rate research
Read More
Sentence embeddings encode information relating to the usage of idioms in a sentence. This paper reports a set of experiments that combine a probing methodology with input masking to analyse where in a sentence this idiomatic information is taken fro
m, and what form it takes. Our results indicate that BERT's idiomatic key is primarily found within an idiomatic expression, but also draws on information from the surrounding context. Also, BERT can distinguish between the disruption in a sentence caused by words missing and the incongruity caused by idiomatic usage.
The dominant approach in probing neural networks for linguistic properties is to train a new shallow multi-layer perceptron (MLP) on top of the model's internal representations. This approach can detect properties encoded in the model, but at the cos
t of adding new parameters that may learn the task directly. We instead propose a subtractive pruning-based probe, where we find an existing subnetwork that performs the linguistic task of interest. Compared to an MLP, the subnetwork probe achieves both higher accuracy on pre-trained models and lower accuracy on random models, so it is both better at finding properties of interest and worse at learning on its own. Next, by varying the complexity of each probe, we show that subnetwork probing Pareto-dominates MLP probing in that it achieves higher accuracy given any budget of probe complexity. Finally, we analyze the resulting subnetworks across various tasks to locate where each task is encoded, and we find that lower-level tasks are captured in lower layers, reproducing similar findings in past work.
Human evaluation for summarization tasks is reliable but brings in issues of reproducibility and high costs. Automatic metrics are cheap and reproducible but sometimes poorly correlated with human judgment. In this work, we propose flexible semiautom
atic to automatic summary evaluation metrics, following the Pyramid human evaluation method. Semi-automatic Lite2Pyramid retains the reusable human-labeled Summary Content Units (SCUs) for reference(s) but replaces the manual work of judging SCUs' presence in system summaries with a natural language inference (NLI) model. Fully automatic Lite3Pyramid further substitutes SCUs with automatically extracted Semantic Triplet Units (STUs) via a semantic role labeling (SRL) model. Finally, we propose in-between metrics, Lite2.xPyramid, where we use a simple regressor to predict how well the STUs can simulate SCUs and retain SCUs that are more difficult to simulate, which provides a smooth transition and balance between automation and manual evaluation. Comparing to 15 existing metrics, we evaluate human-metric correlations on 3 existing meta-evaluation datasets and our newly collected PyrXSum (with 100/10 XSum examples/systems). It shows that Lite2Pyramid consistently has the best summary-level correlations; Lite3Pyramid works better than or comparable to other automatic metrics; Lite2.xPyramid trades off small correlation drops for larger manual effort reduction, which can reduce costs for future data collection.
The proliferation of Hate Speech and misinformation in social media is fast becoming a menace to society. In compliment, the dissemination of hate-diffusing, promising and anti-oppressive messages become a unique alternative. Unfortunately, due to it
s complex nature as well as the relatively limited manifestation in comparison to hostile and neutral content, the identification of Hope Speech becomes a challenge. This work revolves around the detection of Hope Speech in Youtube comments, for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion. We achieve an f-score of 0.93, ranking 1st on the leaderboard for English comments.
Finding informative COVID-19 posts in a stream of tweets is very useful to monitor health-related updates. Prior work focused on a balanced data setup and on English, but informative tweets are rare, and English is only one of the many languages spok
en in the world. In this work, we introduce a new dataset of 5,000 tweets for finding informative COVID-19 tweets for Danish. In contrast to prior work, which balances the label distribution, we model the problem by keeping its natural distribution. We examine how well a simple probabilistic model and a convolutional neural network (CNN) perform on this task. We find a weighted CNN to work well but it is sensitive to embedding and hyperparameter choices. We hope the contributed dataset is a starting point for further work in this direction.
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
