In contrast to such bottom-up approaches, we present GAN-Tree, which follows a hierarchical divisive strategy to address such discontinuous multi-modal data.
![maharshi maharshi](https://www.thetelugufilmnagar.com/wp-content/uploads/2021/03/maharshi.jpg)
Though multi-mode prior or multi-generator models have been proposed to alleviate this problem, such approaches may fail depending on the empirically chosen initial mode components.
![maharshi maharshi](https://www.messagefrommasters.com/Enlightenment/bahgwan-ramana3.jpg)
For HybridQA (Chen et al., 2020b), a dataset that involves large documents containing tables, we improve the best prior result by 19 points.ĭespite the remarkable success of generative adversarial networks, their performance seems less impressive for diverse training sets, requiring learning of discontinuous mapping functions. MATE also has a more appropriate inductive bias for tabular data, and sets a new state-of-the-art for three table reasoning datasets. This architecture scales linearly with respect to speed and memory, and can handle documents containing more than 8000 tokens with current accelerators. MATE uses sparse attention in a way that allows heads to efficiently attend to either rows or columns in a table. Here we propose MATE, a novel Transformer architecture designed to model the structure of web tables.
![maharshi maharshi](https://images.hindustantimes.com/rf/image_size_960x540/HT/p2/2019/05/02/Pictures/_13ed0f78-6c93-11e9-adf4-e14f82ec3649.png)
However, more than 20% of relational tables on the web have 20 or more rows (Cafarella et al., 2008), and these large tables present a challenge for current Transformer models, which are typically limited to 512 tokens. Tables are ubiquitous on the web, and are rich in information. This work presents a sparse-attention Transformer architecture for modeling documents that contain large tables.