correct the URL RST format used in references.md

README.md

@@ -56,7 +56,7 @@ or via python module (`python -m textattack ...`).
 > dataset samples, and the configuration file `config.yaml`. To change the cache path, set the 
 > environment variable `TA_CACHE_DIR`. (for example: `TA_CACHE_DIR=/tmp/ textattack attack ...`).
 
-## Usage  `textattack --help`
+## Usage:  `textattack --help`
 
 TextAttack's main features can all be accessed via the `textattack` command. Two very
 common commands are `textattack attack <args>`, and `textattack augment <args>`. You can see more
@@ -71,7 +71,7 @@ textattack attack --help
 
 The [`examples/`](examples/) folder includes scripts showing common TextAttack usage for training models, running attacks, and augmenting a CSV file. The [documentation website](https://textattack.readthedocs.io/en/latest) contains walkthroughs explaining basic usage of TextAttack, including building a custom transformation and a custom constraint..
 
-### Running Attacks `textattack attack --help`
+### Running Attacks: `textattack attack --help`
 
 The easiest way to try out an attack is via the command-line interface, `textattack attack`. 
 
@@ -99,7 +99,7 @@ textattack attack --model lstm-mr --num-examples 20 \
 
 > **Tip:** Instead of specifying a dataset and number of examples, you can pass `--interactive` to attack samples inputted by the user.
 
-### Attacks and Papers Implemented ("Attack Recipes") `textattack attack --recipe [recipe_name]`
+### Attacks and Papers Implemented ("Attack Recipes"): `textattack attack --recipe [recipe_name]`
 
 We include attack recipes which implement attacks from the literature. You can list attack recipes using `textattack list attack-recipes`.
 
@@ -291,7 +291,7 @@ textattack attack --model bert-base-uncased-sst2 --recipe textfooler --num-examp
  textattack attack --model t5-en-de --recipe seq2sick --num-examples 100
 ```
 
-### Augmenting Text `textattack augment`
+### Augmenting Text: `textattack augment`
 
 Many of the components of TextAttack are useful for data augmentation. The `textattack.Augmenter` class
 uses a transformation and a list of constraints to augment data. We also offer five built-in recipes
@@ -365,7 +365,7 @@ You can also create your own augmenter from scratch by importing transformations
 ['What I cannot creae, I do not understand.', 'What I cannot creat, I do not understand.', 'What I cannot create, I do not nderstand.', 'What I cannot create, I do nt understand.', 'Wht I cannot create, I do not understand.']
 ```
 
-### Training Models  `textattack train`
+### Training Models:  `textattack train`
 
 Our model training code is available via `textattack train` to help you train LSTMs,
 CNNs, and `transformers` models using TextAttack out-of-the-box. Datasets are
@@ -388,12 +388,13 @@ This uses the `EasyDataAugmenter` recipe to augment the `rotten_tomatoes` datase
 textattack train --model bert-base-uncased --dataset glue^cola --batch-size 32 --epochs 5
 ```
 
-### `textattack peek-dataset`
+
+### To check datasets: `textattack peek-dataset`
 
 To take a closer look at a dataset, use `textattack peek-dataset`. TextAttack will print some cursory statistics about the inputs and outputs from the dataset. For example, `textattack peek-dataset --dataset-from-huggingface snli` will show information about the SNLI dataset from the NLP package.
 
 
-### To list components `textattack list`
+### To list components: `textattack list`
 
 There are lots of pieces in TextAttack, and it can be difficult to keep track of all of them. You can use `textattack list` to list components, for example, pretrained models (`textattack list models`) or available search methods (`textattack list search-methods`).
 
@@ -474,7 +475,7 @@ You can then run attacks on samples from this dataset by adding the argument `--
 
 
 
-### Attacks
+### Attacks and how to design a new attack 
 
 The `attack_one` method in an `Attack` takes as input an `AttackedText`, and outputs either a `SuccessfulAttackResult` if it succeeds or a `FailedAttackResult` if it fails. 
 

docs/1start/references.md

@@ -5,9 +5,9 @@ How to Cite TextAttack
 
 - Paper [EMNLP Demo](https://arxiv.org/abs/2005.05909)
 
-- Abstract: While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop NLP attacks and utilize them to improve model performance. This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP. TextAttack builds attacks from four components: a goal function, a set of constraints, a transformation, and a search method. TextAttack's modular design enables researchers to easily construct attacks from combinations of novel and existing components. TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets, including BERT and other transformers, and all GLUE tasks. TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness. TextAttack is democratizing NLP: anyone can try data augmentation and adversarial training on any model or dataset, with just a few lines of code. Code and tutorials are available at this site.
+- Abstract: An adversarial example is an input designed to fool a machine learning model. While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop NLP attacks and utilize them to improve model performance. This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP. TextAttack builds attacks from four components: a goal function, a set of constraints, a transformation, and a search method. TextAttack's modular design enables researchers to easily construct attacks from combinations of novel and existing components. TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets, including BERT and other transformers, and all GLUE tasks. TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness. TextAttack is democratizing NLP: anyone can try data augmentation and adversarial training on any model or dataset, with just a few lines of code. Code and tutorials are available at this site.
 
-### Our Github on TextAttack: `TextAttack <https://github.com/QData/TextAttack>`_
+### Our Github on TextAttack: [https://github.com/QData/TextAttack](https://github.com/QData/TextAttack)
 
 - Citations
 
@@ -29,7 +29,7 @@ How to Cite TextAttack
 
 - Abstract:  State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a successful attack. We distill ideas from past work into a unified framework: a successful natural language adversarial example is a perturbation that fools the model and follows some linguistic constraints. We then analyze the outputs of two state-of-the-art synonym substitution attacks. We find that their perturbations often do not preserve semantics, and 38% introduce grammatical errors. Human surveys reveal that to successfully preserve semantics, we need to significantly increase the minimum cosine similarities between the embeddings of swapped words and between the sentence encodings of original and perturbed sentences.With constraints adjusted to better preserve semantics and grammaticality, the attack success rate drops by over 70 percentage points.
 
-### Our Github on Reevaluation: `Reevaluating-NLP-Adversarial-Examples Github <https://github.com/QData/Reevaluating-NLP-Adversarial-Examples>`__ 
+### Our Github on Reevaluation: [Reevaluating-NLP-Adversarial-Examples Github](https://github.com/QData/Reevaluating-NLP-Adversarial-Examples)
 
 - Citations
 ```
@@ -49,7 +49,7 @@ How to Cite TextAttack
 
 - Abstract:  We study the behavior of several black-box search algorithms used for generating adversarial examples for natural language processing (NLP) tasks. We perform a fine-grained analysis of three elements relevant to search: search algorithm, search space, and search budget. When new search methods are proposed in past work, the attack search space is often modified alongside the search method. Without ablation studies benchmarking the search algorithm change with the search space held constant, an increase in attack success rate could from an improved search method or a less restrictive search space. Additionally, many previous studies fail to properly consider the search algorithms' run-time cost, which is essential for downstream tasks like adversarial training. Our experiments provide a reproducible benchmark of search algorithms across a variety of search spaces and query budgets to guide future research in adversarial NLP. Based on our experiments, we recommend greedy attacks with word importance ranking when under a time constraint or attacking long inputs, and either beam search or particle swarm optimization otherwise. 
 
-### Our Github on benchmarking:  `TextAttack-Search-Benchmark Github <https://github.com/QData/TextAttack-Search-Benchmark>`__ 
+### Our Github on benchmarking:  [TextAttack-Search-Benchmark Github](https://github.com/QData/TextAttack-Search-Benchmark)
 
 
 - Citations: