Curriculum learning, a machine training strategy that feeds training instances to the model from easy to hard, has been proven to facilitate the dialogue generation task. Meanwhile, knowledge distillation, a knowledge transformation methodology among
teachers and students networks can yield significant performance boost for student models. Hence, in this paper, we introduce a combination of curriculum learning and knowledge distillation for efficient dialogue generation models, where curriculum learning can help knowledge distillation from data and model aspects. To start with, from the data aspect, we cluster the training cases according to their complexity, which is calculated by various types of features such as sentence length and coherence between dialog pairs. Furthermore, we employ an adversarial training strategy to identify the complexity of cases from model level. The intuition is that, if a discriminator can tell the generated response is from the teacher or the student, then the case is difficult that the student model has not adapted to yet. Finally, we use self-paced learning, which is an extension to curriculum learning to assign weights for distillation. In conclusion, we arrange a hierarchical curriculum based on the above two aspects for the student model under the guidance from the teacher model. Experimental results demonstrate that our methods achieve improvements compared with competitive baselines.
In supervised learning, a well-trained model should be able to recover ground truth accurately, i.e. the predicted labels are expected to resemble the ground truth labels as much as possible. Inspired by this, we formulate a difficulty criterion base
d on the recovery degrees of training examples. Motivated by the intuition that after skimming through the training corpus, the neural machine translation (NMT) model knows'' how to schedule a suitable curriculum according to learning difficulty, we propose a self-guided curriculum learning strategy that encourages the NMT model to learn from easy to hard on the basis of recovery degrees. Specifically, we adopt sentence-level BLEU score as the proxy of recovery degree. Experimental results on translation benchmarks including WMT14 English-German and WMT17 Chinese-English demonstrate that our proposed method considerably improves the recovery degree, thus consistently improving the translation performance.
Machine Translation (MT) systems often fail to preserve different stylistic and pragmatic properties of the source text (e.g. sentiment and emotion and gender traits and etc.) to the target and especially in a low-resource scenario. Such loss can aff
ect the performance of any downstream Natural Language Processing (NLP) task and such as sentiment analysis and that heavily relies on the output of the MT systems. The susceptibility to sentiment polarity loss becomes even more severe when an MT system is employed for translating a source content that lacks a legitimate language structure (e.g. review text). Therefore and we must find ways to minimize the undesirable effects of sentiment loss in translation without compromising with the adequacy. In our current work and we present a deep re-inforcement learning (RL) framework in conjunction with the curriculum learning (as per difficulties of the reward) to fine-tune the parameters of a pre-trained neural MT system so that the generated translation successfully encodes the underlying sentiment of the source without compromising the adequacy unlike previous methods. We evaluate our proposed method on the English--Hindi (product domain) and French--English (restaurant domain) review datasets and and found that our method brings a significant improvement over several baselines in the machine translation and and sentiment classification tasks.
This paper investigates and reveals the relationship between two closely related machine learning disciplines, namely Active Learning (AL) and Curriculum Learning (CL), from the lens of several novel curricula. This paper also introduces Active Curri
culum Learning (ACL) which improves AL by combining AL with CL to benefit from the dynamic nature of the AL informativeness concept as well as the human insights used in the design of the curriculum heuristics. Comparison of the performance of ACL and AL on two public datasets for the Named Entity Recognition (NER) task shows the effectiveness of combining AL and CL using our proposed framework.
This paper takes a first step towards a critical thinking curriculum for neural auto-regressive language models. We introduce a synthetic corpus of deductively valid arguments, and generate artificial argumentative texts to train CRiPT: a critical th
inking intermediarily pre-trained transformer based on GPT-2. Significant transfer learning effects can be observed: Trained on three simple core schemes, CRiPT accurately completes conclusions of different, and more complex types of arguments, too. CRiPT generalizes the core argument schemes in a correct way. Moreover, we obtain consistent and promising results for NLU benchmarks. In particular, CRiPT's zero-shot accuracy on the GLUE diagnostics exceeds GPT-2's performance by 15 percentage points. The findings suggest that intermediary pre-training on texts that exemplify basic reasoning abilities (such as typically covered in critical thinking textbooks) might help language models to acquire a broad range of reasoning skills. The synthetic argumentative texts presented in this paper are a promising starting point for building such a critical thinking curriculum for language models.''
While Curriculum Learning (CL) has recently gained traction in Natural language Processing Tasks, it is still not adequately analyzed. Previous works only show their effectiveness but fail short to explain and interpret the internal workings fully. I
n this paper, we analyze curriculum learning in sentiment analysis along multiple axes. Some of these axes have been proposed by earlier works that need more in-depth study. Such analysis requires understanding where curriculum learning works and where it does not. Our axes of analysis include Task difficulty on CL, comparing CL pacing techniques, and qualitative analysis by visualizing the movement of attention scores in the model as curriculum phases progress. We find that curriculum learning works best for difficult tasks and may even lead to a decrement in performance for tasks with higher performance without curriculum learning. We see that One-Pass curriculum strategies suffer from catastrophic forgetting and attention movement visualization within curriculum pacing. This shows that curriculum learning breaks down the challenging main task into easier sub-tasks solved sequentially.
The study aimed to determine the degree of availability of
quality standards for the components of the university curriculum
according to the approach of curriculum engineering, from the
point of view of the faculty and technical staff in a number
of
faculties at Al-Baath University, in addition to the difference in
views on the degree of availability of the standards according to
the variable type of college.
The research aims to identify the degree of the availability of
sentimental goals in the developed curriculum for kindergarten in
Syria. To achieve the aim, the descriptive approach and a list of
sentimental goals by the researcher Ruba Aldrgla we
re adopted.
Therefore, an analytical content form prepared by the researcher
was used to verify the veracity and persistence of the analysis,
which both are achieved. The list includes 86 sentimental sub-goals
classified into 12 major goals.
Purpose: Knowing the different elements, which affect the teaching methods in the
university, is considered as a basic element to improve these methods. Hence, this study
aims to evaluate the teaching process and the clinical application of oral me
dicine material
at dentistry faculty. In addition, this study examines the effectiveness of oral medicine
sheet (patient file) in reflecting the student knowledge into correct scientific identification
of the remedial cases at University.
Materials and Methods: The study was conducted among 45 undergraduate
students in the last year of the dental program at the dental faculty in The Syrian Private
University.
Results and Conclusion: The results show that the used oral medicine sheet is not
good in general and that is evaluation by the person using it correlates significantly with
his activity level. In addition, they show that what decides the sheet quality is the difficulty
of filling in it and its ability to help student to reach the good diagnosis of remedial case.
This study aimed Evaluation of social studies curriculum in eighthgrade
on light of some contemporary global trends from the point
of view of teachers.
