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Register a new userFinetuning Pretrained Transformers into Variational Autoencoders
finetuning المحولات مسبقا إلى السيارات السيارات المتنوعة
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أصبحت السيارات التلقائية النصية النصية (VAES) سيئة السمعة بالنسبة للانهيار الخلفي، وهي ظاهرة حيث يتعلم وحدة فك ترميز النموذج أن تجاهل الإشارات من التشفير.نظرا لأنه من المعروف أن الانهيار الخلفي يتم تفاقمه من خلال أجهزة فك ترميز التعبير، فقد شهدت المحولات اعتمادا محدودا كمكون مكونات في VAES النصية.الدراسات القائمة التي تضم المحولات في مبيعات النصوص (لي وآخرون، 2020؛ فانغ وآخرون.، 2021) تخفيف الانهيار الخلفي باستخدام محاولات ضخمة، وهي تقنية غير متوفرة لمعظم مجتمع البحث دون موارد حوسبة واسعة النطاق.نقدم خطة تدريبية بسيطة من مرحلتين لتحويل محول تسلسل إلى تسلسل إلى VIE مع Finetuning فقط.النموذج اللغوي الناتج هو تنافسية مع VAES المستندة إلى المحولات بشكل كبير في بعض المقاييس الداخلية مع الوقوع على الآخرين.لتسهيل التدريب، استكشفنا بشكل شامل تأثير تقنيات تخفيف الطيام الخلفي المشترك في الأدب.نطلق سرد كودنا للاستكشاف.
Text variational autoencoders (VAEs) are notorious for posterior collapse, a phenomenon where the model's decoder learns to ignore signals from the encoder. Because posterior collapse is known to be exacerbated by expressive decoders, Transformers have seen limited adoption as components of text VAEs. Existing studies that incorporate Transformers into text VAEs (Li et al., 2020; Fang et al., 2021) mitigate posterior collapse using massive pretraining, a technique unavailable to most of the research community without extensive computing resources. We present a simple two-phase training scheme to convert a sequence-to-sequence Transformer into a VAE with just finetuning. The resulting language model is competitive with massively pretrained Transformer-based VAEs in some internal metrics while falling short on others. To facilitate training we comprehensively explore the impact of common posterior collapse alleviation techniques in the literature. We release our code for reproducability.
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Variational autoencoders have been studied as a promising approach to model one-to-many mappings from context to response in chat response generation. However, they often fail to learn proper mappings. One of the reasons for this failure is the discr
epancy between a response and a latent variable sampled from an approximated distribution in training. Inappropriately sampled latent variables hinder models from constructing a modulated latent space. As a result, the models stop handling uncertainty in conversations. To resolve that, we propose speculative sampling of latent variables. Our method chooses the most probable one from redundantly sampled latent variables for tying up the variable with a given response. We confirm the efficacy of our method in response generation with massive dialogue data constructed from Twitter posts.
It has been long known that sparsity is an effective inductive bias for learning efficient representation of data in vectors with fixed dimensionality, and it has been explored in many areas of representation learning. Of particular interest to this
work is the investigation of the sparsity within the VAE framework which has been explored a lot in the image domain, but has been lacking even a basic level of exploration in NLP. Additionally, NLP is also lagging behind in terms of learning sparse representations of large units of text e.g., sentences. We use the VAEs that induce sparse latent representations of large units of text to address the aforementioned shortcomings. First, we move in this direction by measuring the success of unsupervised state-of-the-art (SOTA) and other strong VAE-based sparsification baselines for text and propose a hierarchical sparse VAE model to address the stability issue of SOTA. Then, we look at the implications of sparsity on text classification across 3 datasets, and highlight a link between performance of sparse latent representations on downstream tasks and its ability to encode task-related information.
The ability of learning disentangled representations represents a major step for interpretable NLP systems as it allows latent linguistic features to be controlled. Most approaches to disentanglement rely on continuous variables, both for images and
text. We argue that despite being suitable for image datasets, continuous variables may not be ideal to model features of textual data, due to the fact that most generative factors in text are discrete. We propose a Variational Autoencoder based method which models language features as discrete variables and encourages independence between variables for learning disentangled representations. The proposed model outperforms continuous and discrete baselines on several qualitative and quantitative benchmarks for disentanglement as well as on a text style transfer downstream application.
This shared task system description depicts two neural network architectures submitted to the ProfNER track, among them the winning system that scored highest in the two sub-tasks 7a and 7b. We present in detail the approach, preprocessing steps and
the architectures used to achieve the submitted results, and also provide a GitHub repository to reproduce the scores. The winning system is based on a transformer-based pretrained language model and solves the two sub-tasks simultaneously.
Unsupervised relation extraction works by clustering entity pairs that have the same relations in the text. Some existing variational autoencoder (VAE)-based approaches train the relation extraction model as an encoder that generates relation classif
ications. A decoder is trained along with the encoder to reconstruct the encoder input based on the encoder-generated relation classifications. These classifications are a latent variable so they are required to follow a pre-defined prior distribution which results in unstable training. We propose a VAE-based unsupervised relation extraction technique that overcomes this limitation by using the classifications as an intermediate variable instead of a latent variable. Specifically, classifications are conditioned on sentence input, while the latent variable is conditioned on both the classifications and the sentence input. This allows our model to connect the decoder with the encoder without putting restrictions on the classification distribution; which improves training stability. Our approach is evaluated on the NYT dataset and outperforms state-of-the-art methods.
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