Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNeural-based RST Parsing And Analysis In Persuasive Discourse
التحليلات والتحليلات الأولية القائمة على الحلق في الخطاب المقنع
256
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
ركزت معظم الدراسات الحالية للاستخدام اللغوي في محتوى الوسائط الاجتماعية على الميزات اللغوية على مستوى السطح (على سبيل المثال، كلمات الوظائف وعلامات الترقيم) وجوانب المستوى الدلالي (على سبيل المثال، الموضوعات والمعنويات والعواطف) للتعليقات. لم يتم استكشاف استراتيجيات الكاتب لبناء وربط قطاعات النص على نطاق واسع على الرغم من أن هذه المعرفة من المتوقع أن تتخلص الضوء على كيفية سبب الناس في البيئات عبر الإنترنت. المساهمة في هذا الاتجاه التحليل لدراسات وسائل التواصل الاجتماعي، نبني نظام تحليل عصبي عصبي يمكن الوصول إليه علنا يحلل علاقات الخطاب في تعليق عبر الإنترنت. توضح تجاربنا أن هذا النظام يحقق أداء مماثل بين جميع أنظمة التحليل العصبي العصبي. لإظهار استخدام هذه الأداة في تحليل وسائل التواصل الاجتماعي، فإننا نطبق عليه لتحديد علاقات الخطاب في تعليقات مقنعة وغير مقنعة وفحص العلاقات بين عمق شجرة الخطاب الثنائي، وعلاقات الخطاب، والإقناع المتصور للتعليقات عبر الإنترنت. يوضح عملنا إمكانية تحليل هياكل خطاب التعليقات عبر الإنترنت مع نظامنا وآثار هذه الهياكل لفهم الاتصالات عبر الإنترنت.
Most of the existing studies of language use in social media content have focused on the surface-level linguistic features (e.g., function words and punctuation marks) and the semantic level aspects (e.g., the topics, sentiment, and emotions) of the comments. The writer's strategies of constructing and connecting text segments have not been widely explored even though this knowledge is expected to shed light on how people reason in online environments. Contributing to this analysis direction for social media studies, we build an openly accessible neural RST parsing system that analyzes discourse relations in an online comment. Our experiments demonstrate that this system achieves comparable performance among all the neural RST parsing systems. To demonstrate the use of this tool in social media analysis, we apply it to identify the discourse relations in persuasive and non-persuasive comments and examine the relationships among the binary discourse tree depth, discourse relations, and the perceived persuasiveness of online comments. Our work demonstrates the potential of analyzing discourse structures of online comments with our system and the implications of these structures for understanding online communications.
References used
https://aclanthology.org/
rate research
Read More
Sentence splitting involves the segmentation of a sentence into two or more shorter sentences. It is a key component of sentence simplification, has been shown to help human comprehension and is a useful preprocessing step for NLP tasks such as summa
risation and relation extraction. While several methods and datasets have been proposed for developing sentence splitting models, little attention has been paid to how sentence splitting interacts with discourse structure. In this work, we focus on cases where the input text contains a discourse connective, which we refer to as discourse-based sentence splitting. We create synthetic and organic datasets for discourse-based splitting and explore different ways of combining these datasets using different model architectures. We show that pipeline models which use discourse structure to mediate sentence splitting outperform end-to-end models in learning the various ways of expressing a discourse relation but generate text that is less grammatical; that large scale synthetic data provides a better basis for learning than smaller scale organic data; and that training on discourse-focused, rather than on general sentence splitting data provides a better basis for discourse splitting.
Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summar
izers. In particular, we generate unlabeled RST-style discourse trees from the self-attention matrices of the transformer model. Experiments across models and datasets reveal that the summarizer learns both, dependency- and constituency-style discourse information, which is typically encoded in a single head, covering long- and short-distance discourse dependencies. Overall, the experimental results suggest that the learned discourse information is general and transferable inter-domain.
Abstract Interpretable rationales for model predictions are crucial in practical applications. We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where dependenc
y edges are extracted and labeled by comparing them to edges in a training set. The training edges are explicitly used for the predictions; thus, it is easy to grasp the contribution of each edge to the predictions. Our experiments show that our instance-based models achieve competitive accuracy with standard neural models and have the reasonable plausibility of instance-based explanations.
Machine reading comprehension (MRC) is one of the most challenging tasks in natural language processing domain. Recent state-of-the-art results for MRC have been achieved with the pre-trained language models, such as BERT and its modifications. Despi
te the high performance of these models, they still suffer from the inability to retrieve correct answers from the detailed and lengthy passages. In this work, we introduce a novel scheme for incorporating the discourse structure of the text into a self-attention network, and, thus, enrich the embedding obtained from the standard BERT encoder with the additional linguistic knowledge. We also investigate the influence of different types of linguistic information on the model's ability to answer complex questions that require deep understanding of the whole text. Experiments performed on the SQuAD benchmark and more complex question answering datasets have shown that linguistic enhancing boosts the performance of the standard BERT model significantly.
Discourse analysis has long been known to be fundamental in natural language processing. In this research, we present our insight on discourse-level topic chain (DTC) parsing which aims at discovering new topics and investigating how these topics evo
lve over time within an article. To address the lack of data, we contribute a new discourse corpus with DTC-style dependency graphs annotated upon news articles. In particular, we ensure the high reliability of the corpus by utilizing a two-step annotation strategy to build the data and filtering out the annotations with low confidence scores. Based on the annotated corpus, we introduce a simple yet robust system for automatic discourse-level topic chain parsing.
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
