Enable doc in Spanish (#16518)

* Reorganize doc for multilingual support

* Fix style

* Style

* Toc trees

* Adapt templates

docs/source/en/model_doc/dialogpt.mdx

@@ -0,0 +1,49 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# DialoGPT
+
+## Overview
+
+DialoGPT was proposed in [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao,
+Jianfeng Gao, Jingjing Liu, Bill Dolan. It's a GPT2 Model trained on 147M conversation-like exchanges extracted from
+Reddit.
+
+The abstract from the paper is the following:
+
+*We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained
+transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning
+from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human
+both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems
+that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline
+systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response
+generation and the development of more intelligent open-domain dialogue systems.*
+
+Tips:
+
+- DialoGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
+  than the left.
+- DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful
+  at response generation in open-domain dialogue systems.
+- DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on [DialoGPT's model card](https://huggingface.co/microsoft/DialoGPT-medium).
+
+Training:
+
+In order to train or fine-tune DialoGPT, one can use causal language modeling training. To cite the official paper: *We
+follow the OpenAI GPT-2 to model a multiturn dialogue session as a long text and frame the generation task as language
+modeling. We first concatenate all dialog turns within a dialogue session into a long text x_1,..., x_N (N is the
+sequence length), ended by the end-of-text token.* For more information please confer to the original paper.
+
+
+DialoGPT's architecture is based on the GPT2 model, so one can refer to [GPT2's documentation page](gpt2).
+
+The original code can be found [here](https://github.com/microsoft/DialoGPT).