Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe ISA-17 Quantification Challenge: Background and introduction
التحدي الكمي ISA-17: الخلفية المقدمة
110
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
توفر هذه الورقة، مخصصة لمسار التعليق الكمي ISA-17، معلومات أساسية لمهمة التوضيح الكمي المشتركة في ورشة عمل ISA-17، A.AK.A. التحدي الكمي.على وجه الخصوص، يتم تفسير دور بناء الجملة التجريدي والخرداني لغتها بلغة ترميز الكم والكمية، والتفسير الدلالي التوضيحية للكمية فيما يتعلق بمبادئ ISO التوضيحية الدلالية.بالإضافة إلى ذلك، يحفز الاختيار بمجموعة الاختبار من التحدي الكمي، إلى جانب الأسواق المقترحة لجمل الجناح.
This paper, intended for the ISA-17 Quantification Annotation track, provides background information for the shared quantification annotation task at the ISA-17 workshop, a.k.a. the Quantification Challenge. In particular, the role of the abstract and concrete syntax of the QuantML markup language are explained, and the semantic interpretation of QuantML annotations in relation to the ISO principles of semantic annotation. Additionally, the choice is motivated of the test suite of the Quantification Challenge, along with the suggested markables for the sentences of the suite.
References used
https://aclanthology.org/
rate research
Read More
Background subtraction (BS) is the first step of various computer vision application specially those depending on motion tracking such as (car tacking, human recognition…etc.).Indeed, videos captured outdoors may contain a lot of undesirable changes
‘wind impact, illumination changes, weather conditions and others ’, generate numerous false positives.
This paper presents comparison between the simplest method for background extraction (background subtraction) and Gaussian Mixture Model which is common method in outdoors videos. These two method are then compared based on the ability of each one to detect moving object in outdoors videos especially with presence and absence of shadow in addition to other challenges like object movement in background, wind effect and camera instability. The results of this comparison is used to determine the suitable method for each state.
This paper presents our multilingual dependency parsing system as used in the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies. Our system consists of an unfactorized biaffine classifier that operates directly on fine-tuned XLM-R
embeddings and generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens. To avoid sparsity issues resulting from lexicalized dependency labels, we replace lexical items in relations with placeholders at training and prediction time, later retrieving them from the parse via a hybrid rule-based/machine-learning system. In addition, we utilize model ensembling at prediction time. Our system achieves high parsing accuracy on the blind test data, ranking 3rd out of 9 with an average ELAS F1 score of 86.97.
We propose a shared task on training instance selection for few-shot neural text generation. Large-scale pretrained language models have led to dramatic improvements in few-shot text generation. Nonetheless, almost all previous work simply applies ra
ndom sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. Studying the selection strategy can help us (1) make the most use of our annotation budget in downstream tasks and (2) better benchmark few-shot text generative models. We welcome submissions that present their selection strategies and the effects on the generation quality.
The researcher found that the process of organizational change is
affected by the levels of administrative creativity.
Languages differ in terms of the absence or presence of gender features, the number of gender classes and whether and where gender features are explicitly marked. These cross-linguistic differences can lead to ambiguities that are difficult to resolv
e, especially for sentence-level MT systems. The identification of ambiguity and its subsequent resolution is a challenging task for which currently there aren't any specific resources or challenge sets available. In this paper, we introduce gENder-IT, an English--Italian challenge set focusing on the resolution of natural gender phenomena by providing word-level gender tags on the English source side and multiple gender alternative translations, where needed, on the Italian target side.
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
