In cross-lingual text classification, it is required that task-specific training data in high-resource source languages are available, where the task is identical to that of a low-resource target language. However, collecting such training data can b
e infeasible because of the labeling cost, task characteristics, and privacy concerns. This paper proposes an alternative solution that uses only task-independent word embeddings of high-resource languages and bilingual dictionaries. First, we construct a dictionary-based heterogeneous graph (DHG) from bilingual dictionaries. This opens the possibility to use graph neural networks for cross-lingual transfer. The remaining challenge is the heterogeneity of DHG because multiple languages are considered. To address this challenge, we propose dictionary-based heterogeneous graph neural network (DHGNet) that effectively handles the heterogeneity of DHG by two-step aggregations, which are word-level and language-level aggregations. Experimental results demonstrate that our method outperforms pretrained models even though it does not access to large corpora. Furthermore, it can perform well even though dictionaries contain many incorrect translations. Its robustness allows the usage of a wider range of dictionaries such as an automatically constructed dictionary and crowdsourced dictionary, which are convenient for real-world applications.
Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision. Typically, the wrongly labeled instances comprise num
bers of incomplete and inaccurate annotations, while most prior denoising works are only concerned with one kind of noise and fail to fully explore useful information in the training set. To address this issue, we propose a robust learning paradigm named Self-Collaborative Denoising Learning (SCDL), which jointly trains two teacher-student networks in a mutually-beneficial manner to iteratively perform noisy label refinery. Each network is designed to exploit reliable labels via self denoising, and two networks communicate with each other to explore unreliable annotations by collaborative denoising. Extensive experimental results on five real-world datasets demonstrate that SCDL is superior to state-of-the-art DS-NER denoising methods.
In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framew
ork for predicting lexical complexity. In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words
In this paper, we present our systems submitted to SemEval-2021 Task 1 on lexical complexity prediction.The aim of this shared task was to create systems able to predict the lexical complexity of word tokens and bigram multiword expressions within a
given sentence context, a continuous value indicating the difficulty in understanding a respective utterance. Our approach relies on gradient boosted regression tree ensembles fitted using a heterogeneous feature set combining linguistic features, static and contextualized word embeddings, psycholinguistic norm lexica, WordNet, word- and character bigram frequencies and inclusion in wordlists to create a model able to assign a word or multiword expression a context-dependent complexity score. We can show that especially contextualised string embeddings can help with predicting lexical complexity.
We introduce a taxonomic study of parallel programming models
on High-Performance architectures. We review the parallel
architectures(shared and distributed memory), and then the
development of the architectures through the emergence of the
heter
ogeneous and hybrid parallel architectures.
We review important parallel programming model as the
Partitioned Global Address Space (PGAS) model, as model for
distributed memory architectures and the Data Flow model as
model to heterogeneous and hybrid parallel programming. Finally
we present several scenarios for the use of this taxonomic study.
The Reaction of Glycerol carried out with tow aryl halides
under basic conditions (K2CO3) using 5 mol% of Pd(II)Complex as
catalyst, Pd(PPh3)2Cl2. The typical reaction has been performed
between glycerol and bromobenzene. This reaction is achieved
in
water as solvent. However, the catalyst complex does not dissolve
in aqueous solutions rather it acts as heterogeneous catalyst.
Therefore, it is filtrated at the end of the reaction and reused several
times. Accordingly, new compound were prepared by reacting
glycerol with 1-bromo-2,6-dichlorobenzene. It is anticipated that the
synthesized compounds may have pharmaceutical application.
A product study of the reaction of some five-membered heteroaromatic
primary amines with formalin in Acetonitrile was made: Aminols of general
formula (G-NH-CH٢OH) were identified in most cases at pH (٧،٥-٩) in good
yield. Reaction of ٥-methyl ٣-A
minoisoxazol with formalin under basis media
(pH~٩) give the Aminal (G-NH-CH٢-NH-G).
