Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userIncorporating tone in the calculation of phonotactic probability
دمج لهجة في حساب الاحتمالات الشوئية
499
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تحقق هذه الورقة في كيفية تأثير ترتيب النغمة بالنسبة إلى السلسلة القطاعية على حساب الاحتمالات الشوئية.تم تدريب نماذج الشبكة العصبية العصبية المتكررة على معجم مقطع لفظي من مقطع لفظي لأربعة لغات من مقطع لفظي آسيوي (الماندرين والتايلاندية والفيتنامية والكانتونية) التي تم التعامل معها على أنها شريحة تحدث في مواقف مختلفة في السلسلة.بالنسبة لنماذج Trigram، تفاعل التقليب الأمثل مع اللغة، في حين أن نماذج الشبكة العصبية غير متأثرة نسبيا عن طريق النغمة بجميع اللغات.بالإضافة إلى توفير خط أساس للتقييم في المستقبل، تشير هذه النتائج إلى أن الاحتمالية الشوئية قوية في خيارات كيفية طلب النغمة فيما يتعلق بالعناصر الأخرى في مقطع لفظي.
This paper investigates how the ordering of tone relative to the segmental string influences the calculation of phonotactic probability. Trigram and recurrent neural network models were trained on syllable lexicons of four Asian syllable-tone languages (Mandarin, Thai, Vietnamese, and Cantonese) in which tone was treated as a segment occurring in different positions in the string. For trigram models, the optimal permutation interacted with language, while neural network models were relatively unaffected by tone position in all languages. In addition to providing a baseline for future evaluation, these results suggest that phonotactic probability is robust to choices of how tone is ordered with respect to other elements in the syllable.
References used
https://aclanthology.org/
rate research
Read More
This paper presents an analytical study of the Bond stress and its
relationship with relative slip between the concrete and steel using
finite element method.
Moreover, it presents a probabilistic study using statistics theory on
the data as a re
sult from analytical study to get the bond stress-slip
relationship, which represents the bond equation.
In recent years pre-trained language models (PLM) such as BERT have proven to be very effective in diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question Answering. Trained with massive general-domain text, these pre-traine
d language models capture rich syntactic, semantic and discourse information in the text. However, due to the differences between general and specific domain text (e.g., Wikipedia versus clinic notes), these models may not be ideal for domain-specific tasks (e.g., extracting clinical relations). Furthermore, it may require additional medical knowledge to understand clinical text properly. To solve these issues, in this research, we conduct a comprehensive examination of different techniques to add medical knowledge into a pre-trained BERT model for clinical relation extraction. Our best model outperforms the state-of-the-art systems on the benchmark i2b2/VA 2010 clinical relation extraction dataset.
During the study of liquid filling systems’ performance using a
piston driven by a pneumatic piston, all parameters having an
influence should be considered. One of these parameters is the
piston pressure and its influence on the velocity of the p
iston. This
influence will be explained in this paper.
AMR (Abstract Meaning Representation) and EDS (Elementary Dependency Structures) are two popular meaning representations in NLP/NLU. AMR is more abstract and conceptual, while EDS is more low level, closer to the lexical structures of the given sente
nces. It is thus not surprising that EDS parsing is easier than AMR parsing. In this work, we consider using information from EDS parsing to help improve the performance of AMR parsing. We adopt a transition-based parser and propose to add EDS graphs as additional semantic features using a graph encoder composed of LSTM layer and GCN layer. Our experimental results show that the additional information from EDS parsing indeed gives a boost to the performance of the base AMR parser used in our experiments.
Reasoning about tabular information presents unique challenges to modern NLP approaches which largely rely on pre-trained contextualized embeddings of text. In this paper, we study these challenges through the problem of tabular natural language infe
rence. We propose easy and effective modifications to how information is presented to a model for this task. We show via systematic experiments that these strategies substantially improve tabular inference performance.
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
