Generative models like GAN(Goodfellow et al. 2014) and In this work, we present a new way to learn control VAE(Kingma and Welling 2014) have shown remarkable over autoencoder latent distribution with the help of AAE progress in recent years.Generative adversarial networks (Makhzani et al. 2016) which approximates posterior of the have shown state-of-the-art performance in a variety of latent distribution of autoencoder using any arbitrary prior tasks like Image-To-Image translation(Isola et al. 2018), distribution and using (Chen et al. 2016) for learning disentangled video prediction(Liang et al. 2017), Text-to-Image translation(Zhang representation. The previous work by (Wang, Peng, et al. 2017), drug discovery(Hong et al. 2019), and Ko 2019) had used a similar method of learning the latent and privacy-preserving(Shi et al. 2018). VAE has shown prior using AAE along with a perceptual loss and Information state-of-the-art performance in a variety of tasks like image maximization regularizer to train the decoder with generation(Gregor et al. 2015), semi-supervised learning(Maaløe the help of an extra discriminator.

The rapid growth of machine translation (MT) systems has necessitated comprehensive studies to meta-evaluate evaluation metrics being used, which enables a better selection of metrics that best reflect MT quality. Unfortunately, most of the research focuses on high-resource languages, mainly English, the observations for which may not always apply to other languages. Indian languages, having over a billion speakers, are linguistically different from English, and to date, there has not been a systematic study of evaluating MT systems from English into Indian languages. In this paper, we fill this gap by creating an MQM dataset consisting of 7000 fine-grained annotations, spanning 5 Indian languages and 7 MT systems, and use it to establish correlations between annotator scores and scores obtained using existing automatic metrics. Our results show that pre-trained metrics, such as COMET, have the highest correlations with annotator scores. Additionally, we find that the metrics do not adequately capture fluency-based errors in Indian languages, and there is a need to develop metrics focused on Indian languages. We hope that our dataset and analysis will help promote further research in this area.

Improving factual consistency of abstractive summarization has been a widely studied topic. However, most of the prior works on training factuality-aware models have ignored the negative effect it has on summary quality. We propose EFACTSUM (i.e., Effective Factual Summarization), a candidate summary generation and ranking technique to improve summary factuality without sacrificing summary quality. We show that using a contrastive learning framework with our refined candidate summaries leads to significant gains on both factuality and similarity-based metrics. Specifically, we propose a ranking strategy in which we effectively combine two metrics, thereby preventing any conflict during training. Models trained using our approach show up to 6 points of absolute improvement over the base model with respect to FactCC on XSUM and 11 points on CNN/DM, without negatively affecting either similarity-based metrics or absractiveness.

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its transformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robustness analysis results are available publicly on the NL-Augmenter repository (\url{https://github.com/GEM-benchmark/NL-Augmenter}).