Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCorrected CBOW Performs as well as Skip-gram
تصحيح CBOW يؤدي وكذلك تخطي غرام
67
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
ميكولوف وآخرون.(2013A) لاحظ أن تضمين كلمة كاجب مستمرة من الكلمات (CBOW) تميل إلى أشرطة Skip-Gram المعطفية (SG)، وتم الإبلاغ عن هذه النتيجة في أعمال لاحقة.نجد أن هذه الملاحظات مدفوعة بعدم هذه الاختلافات الأساسية في أهدافها التدريبية، ولكن على الأرجح على تطبيقات مراقبة الألعاب السلبية الخاطئة في المكتبات الشعبية مثل التنفيذ الرسمي و Word2VEC.C و Gensim.نظهر أنه بعد تصحيح الخلل في تحديث التدرج CBOW، يمكن للمرء أن يتعلم أن تضمين Word CBOW تنافس تماما مع SG على مختلف المهام الجوهرية والخارجية، بينما تكون عدة مرات أسرع في التدريب.
Mikolov et al. (2013a) observed that continuous bag-of-words (CBOW) word embeddings tend to underperform Skip-gram (SG) embeddings, and this finding has been reported in subsequent works. We find that these observations are driven not by fundamental differences in their training objectives, but more likely on faulty negative sampling CBOW implementations in popular libraries such as the official implementation, word2vec.c, and Gensim. We show that after correcting a bug in the CBOW gradient update, one can learn CBOW word embeddings that are fully competitive with SG on various intrinsic and extrinsic tasks, while being many times faster to train.
References used
https://aclanthology.org/
rate research
Read More
The ability for variation in language use is necessary for speakers to achieve their conversational goals, for instance when referring to objects in visual environments. We argue that diversity should not be modelled as an independent objective in di
alogue, but should rather be a result or by-product of goal-oriented language generation. Different lines of work in neural language generation investigated decoding methods for generating more diverse utterances, or increasing the informativity through pragmatic reasoning. We connect those lines of work and analyze how pragmatic reasoning during decoding affects the diversity of generated image captions. We find that boosting diversity itself does not result in more pragmatically informative captions, but pragmatic reasoning does increase lexical diversity. Finally, we discuss whether the gain in informativity is achieved in linguistically plausible ways.
Character-aware neural language models can capture the relationship between words by exploiting character-level information and are particularly effective for languages with rich morphology. However, these models are usually biased towards informatio
n from surface forms. To alleviate this problem, we propose a simple and effective method to improve a character-aware neural language model by forcing a character encoder to produce word-based embeddings under Skip-gram architecture in a warm-up step without extra training data. We empirically show that the resulting character-aware neural language model achieves obvious improvements of perplexity scores on typologically diverse languages, that contain many low-frequency or unseen words.
Orange is used in the juice industry, yielding important quantities of by products.
Orange peel is analyzed for chemical composition and water holding capacity. Data show
that, it has high amount of crude fiber, phenolic contents and antioxidant ca
pacity, also it
has high level of water holding capacity. Biscuits are prepared from blendes which contain
a different proportion (5, 10, 15, 20 and 25)% of orange peel flour are also evaluate for
physical and sensory characteristics, chemical composition and rheological properties for
this blendes.
The sensory evaluation does not show any significant difference between control and
that adds with 10% of orange peel flour. Physical parameters, namely, diameter, thickness
and spread ratio were tested. The diameter and thickness of orange peel substituted biscuits
were decreased, whereas spread ratio of biscuits increase with increasing levels of it.
The data reveals that incorporation of orange peel powder in biscuits increase crude
fiber, ash, phenolic contents and antioxidant capacity, it decreases the carbohydrate
content. Rheological properties of the blended flour show increase in water absorption and
stability.
Addition of 40ppm xylanase enzyme reduce the hardness value of the sample in
general, it also decrease the spread ratio as compared to control samples with no enzyme
added.
Sensory evaluation results show good overall acceptability scores for the biscuits
contain 10% orange peel with and without xylanase.
Coarse-grained linguistic information, such as named entities or phrases, facilitates adequately representation learning in pre-training. Previous works mainly focus on extending the objective of BERT's Masked Language Modeling (MLM) from masking ind
ividual tokens to contiguous sequences of n tokens. We argue that such contiguously masking method neglects to model the intra-dependencies and inter-relation of coarse-grained linguistic information. As an alternative, we propose ERNIE-Gram, an explicitly n-gram masking method to enhance the integration of coarse-grained information into pre-training. In ERNIE-Gram, n-grams are masked and predicted directly using explicit n-gram identities rather than contiguous sequences of n tokens. Furthermore, ERNIE-Gram employs a generator model to sample plausible n-gram identities as optional n-gram masks and predict them in both coarse-grained and fine-grained manners to enable comprehensive n-gram prediction and relation modeling. We pre-train ERNIE-Gram on English and Chinese text corpora and fine-tune on 19 downstream tasks. Experimental results show that ERNIE-Gram outperforms previous pre-training models like XLNet and RoBERTa by a large margin, and achieves comparable results with state-of-the-art methods. The source codes and pre-trained models have been released at https://github.com/PaddlePaddle/ERNIE.
Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora. We argue that SiMT systems should be trained and tested on real interpretation data. To illustrate this argument, we propose an in
terpretation test set and conduct a realistic evaluation of SiMT trained on offline translations. Our results, on our test set along with 3 existing smaller scale language pairs, highlight the difference of up-to 13.83 BLEU score when SiMT models are evaluated on translation vs interpretation data. In the absence of interpretation training data, we propose a translation-to-interpretation (T2I) style transfer method which allows converting existing offline translations into interpretation-style data, leading to up-to 2.8 BLEU improvement. However, the evaluation gap remains notable, calling for constructing large-scale interpretation corpora better suited for evaluating and developing SiMT systems.
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
