Update NLP README to introduce major components.

PiperOrigin-RevId: 314526747

official/nlp/README.md

-# TensorFlow Natural Language Processing Modelling Toolkit
+# TensorFlow NLP Modelling Toolkit
 
-tensorflow/models/official/nlp provides a [modeling library](modeling) for constructing
-NLP model achitectures, as well as TF2 reference implementations for
-state-of-the-art models.
+This codebase provides a Natrual Language Processing modeling toolkit written in
+[TF2](https://www.tensorflow.org/guide/effective_tf2). It allows researchers and
+developers to reproduce state-of-the-art model results and train custom models
+to experiment new research ideas.
 
-The repository contains the following models, with implementations, pre-trained
-model weights, usage scripts and conversion utilities:
+## Features
 
-* [Albert](albert)
-* [Bert](bert)
-* [NHNet](nhnet)
-* [XLNet](xlnet)
-* [Transformer for translation](transformer)
+* Reusable and modularized modeling building blocks
+* State-of-the-art reproducible
+* Easy to customize and extend
+* End-to-end training
+* Distributed trainable on both GPUs and TPUs
 
-Addtional features:
+## Major components
+
+### Libraries
+
+We provide modeling library to allow users to train custom models for new
+research ideas. Detailed intructions can be found in READMEs in each folder.
+
+* [modeling/](modeling): modeling library that provides building blocks (e.g., Layers, Networks, and Models) that can be assembled into transformer-based achitectures .
+* [data/](data): binaries and utils for input preprocessing, tokenization, etc.
+
+### State-of-the-Art models and examples
+
+We provide SoTA model implementations, pre-trained models, training and
+evaluation examples, and command lines. Detail instructions can be found in the
+READMEs for specific papers.
+
+1. [BERT](bert): [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Devlin et al., 2018
+2. [ALBERT](albert): [A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942) by Lan et al., 2019
+3. [XLNet](xlnet): [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al., 2019
+4. [Transformer for translation](transformer): [Attention Is All You Need](https://arxiv.org/abs/1706.03762) by Vaswani et al., 2017
+5. [NHNet](nhnet): [Generating Representative Headlines for News Stories](https://arxiv.org/abs/2001.09386) by Gu et al, 2020
 
-* Distributed trainable on both multi-GPU and TPU
-* e2e training for custom models, including both pretraining and finetuning.