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Register a new userOn Randomized Classification Layers and Their Implications in Natural Language Generation
على طبقات التصنيف العشوائية وآثارها في توليد اللغة الطبيعية
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في مهام توليد اللغة الطبيعية، يتم استخدام نموذج لغة عصبي لتوليد سلسلة من الكلمات التي تشكل جملة.يمكن اعتبار مصفوفة الوزن الأعلى من طراز اللغة، المعروف باسم طبقة التصنيف، كمجموعة من المتجهات، كل منها يمثل كلمة مستهدفة من قاموس الهدف.يتم تعلم ومكافحة الكلمات المستهدفة، إلى جانب بقية المعلمات النموذجية، أثناء التدريب.في هذه الورقة، نقوم بتحليل الممتلكات المشفرة في المتجهات المستهدفة والسؤال على ضرورة تعلم هذه المتجهات.نقترح تعيين ناقلات المستهدفة بشكل عشوائي وتحديدها على أنها ثابتة حتى يتم إجراء تحديثات للأوزان أثناء التدريب.نظهر أنه من خلال استبعاد ناقلات التحسين، ينخفض عدد المعلمات بشكل كبير مع تأثير هامشي على الأداء.نوضح فعالية طريقتنا في التسمية التوضيحية للصورة والترجمة الآلية.
In natural language generation tasks, a neural language model is used for generating a sequence of words forming a sentence. The topmost weight matrix of the language model, known as the classification layer, can be viewed as a set of vectors, each representing a target word from the target dictionary. The target word vectors, along with the rest of the model parameters, are learned and updated during training. In this paper, we analyze the properties encoded in the target vectors and question the necessity of learning these vectors. We suggest to randomly draw the target vectors and set them as fixed so that no weights updates are being made during training. We show that by excluding the vectors from the optimization, the number of parameters drastically decreases with a marginal effect on the performance. We demonstrate the effectiveness of our method in image-captioning and machine-translation.
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We ask subjects whether they perceive as human-produced a bunch of texts, some of which are actually human-written, while others are automatically generated. We use this data to fine-tune a GPT-2 model to push it to generate more human-like texts, an
d observe that this fine-tuned model produces texts that are indeed perceived more human-like than the original model. Contextually, we show that our automatic evaluation strategy well correlates with human judgements. We also run a linguistic analysis to unveil the characteristics of human- vs machine-perceived language.
The advent of Deep Learning and the availability of large scale datasets has accelerated research on Natural Language Generation with a focus on newer tasks and better models. With such rapid progress, it is vital to assess the extent of scientific p
rogress made and identify the areas/components that need improvement. To accomplish this in an automatic and reliable manner, the NLP community has actively pursued the development of automatic evaluation metrics. Especially in the last few years, there has been an increasing focus on evaluation metrics, with several criticisms of existing metrics and proposals for several new metrics. This tutorial presents the evolution of automatic evaluation metrics to their current state along with the emerging trends in this field by specifically addressing the following questions: (i) What makes NLG evaluation challenging? (ii) Why do we need automatic evaluation metrics? (iii) What are the existing automatic evaluation metrics and how can they be organised in a coherent taxonomy? (iv) What are the criticisms and shortcomings of existing metrics? (v) What are the possible future directions of research?
Knowledge-enriched text generation poses unique challenges in modeling and learning, driving active research in several core directions, ranging from integrated modeling of neural representations and symbolic information in the sequential/hierarchica
l/graphical structures, learning without direct supervisions due to the cost of structured annotation, efficient optimization and inference with massive and global constraints, to language grounding on multiple modalities, and generative reasoning with implicit commonsense knowledge and background knowledge. In this tutorial we will present a roadmap to line up the state-of-the-art methods to tackle these challenges on this cutting-edge problem. We will dive deep into various technical components: how to represent knowledge, how to feed knowledge into a generation model, how to evaluate generation results, and what are the remaining challenges?
We propose an approach to automatically test for originality in generation tasks where no standard automatic measures exist. Our proposal addresses original uses of language, not necessarily original ideas. We provide an algorithm for our approach an
d a run-time analysis. The algorithm, which finds all of the original fragments in a ground-truth corpus and can reveal whether a generated fragment copies an original without attribution, has a run-time complexity of theta(nlogn) where n is the number of sentences in the ground truth.
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this m
oving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.
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