Merge pull request #707 from Arrrlex/fix-markdown-links-in-html-tables

Clean up formatting in HTML tables

docs/3recipes/attack_recipes_cmd.md

@@ -1,40 +1,40 @@
 # Attack Recipes CommandLine Use
 
-We provide a number of pre-built attack recipes, which correspond to attacks from the literature. 
+We provide a number of pre-built attack recipes, which correspond to attacks from the literature.
 
 
 ## Help: `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
-information about all commands using 
+information about all commands using
 ```bash
-textattack --help 
+textattack --help
 ```
 or a specific command using, for example,
 ```bash
 textattack attack --help
 ```
 
-The [`examples/`](https://github.com/QData/TextAttack/tree/master/examples) folder includes scripts showing common TextAttack usage for training models, running attacks, and augmenting a CSV file. 
+The [`examples/`](https://github.com/QData/TextAttack/tree/master/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`
 
-The easiest way to try out an attack is via the command-line interface, `textattack attack`. 
+The easiest way to try out an attack is via the command-line interface, `textattack attack`.
 
 > **Tip:** If your machine has multiple GPUs, you can distribute the attack across them using the `--parallel` option. For some attacks, this can really help performance.
 
 Here are some concrete examples:
 
-*TextFooler on BERT trained on the MR sentiment classification dataset*: 
+*TextFooler on BERT trained on the MR sentiment classification dataset*:
 ```bash
 textattack attack --recipe textfooler --model bert-base-uncased-mr --num-examples 100
 ```
 
-*DeepWordBug on DistilBERT trained on the Quora Question Pairs paraphrase identification dataset*: 
+*DeepWordBug on DistilBERT trained on the Quora Question Pairs paraphrase identification dataset*:
 ```bash
 textattack attack --model distilbert-base-uncased-cola --recipe deepwordbug --num-examples 100
 ```
@@ -76,127 +76,127 @@ To run an attack recipe: `textattack attack --recipe [recipe_name]`
 <td><sub>Percentage of words perturbed, Language Model perplexity, Word embedding distance</sub></td>
 <td><sub>Counter-fitted word embedding swap</sub></td>
 <td><sub>Genetic Algorithm</sub></td>
-<td ><sub>from (["Generating Natural Language Adversarial Examples" (Alzantot et al., 2018)](https://arxiv.org/abs/1804.07998))</sub></td>
+<td ><sub>from <a href="https://arxiv.org/abs/1804.07998">Generating Natural Language Adversarial Examples" (Alzantot et al., 2018)</a></sub></td>
 </tr>
 <tr>
 <td><code>bae</code> <span class="citation" data-cites="garg2020bae"></span></td>
 <td><sub>Untargeted Classification</sub></td>
 <td><sub>USE sentence encoding cosine similarity</sub></td>
 <td><sub>BERT Masked Token Prediction</sub></td>
 <td><sub>Greedy-WIR</sub></td>
-<td ><sub>BERT masked language model transformation attack from (["BAE: BERT-based Adversarial Examples for Text Classification" (Garg & Ramakrishnan, 2019)](https://arxiv.org/abs/2004.01970)). </td>
+<td ><sub>BERT masked language model transformation attack from <a href="https://arxiv.org/abs/2004.01970">"BAE: BERT-based Adversarial Examples for Text Classification" (Garg & Ramakrishnan, 2019)</a>. </td>
 </tr>
 <tr>
 <td><code>bert-attack</code> <span class="citation" data-cites="li2020bertattack"></span></td>
 <td><sub>Untargeted Classification</td>
 <td><sub>USE sentence encoding cosine similarity, Maximum number of words perturbed</td>
 <td><sub>BERT Masked Token Prediction (with subword expansion)</td>
 <td><sub>Greedy-WIR</sub></td>
-<td ><sub> (["BERT-ATTACK: Adversarial Attack Against BERT Using BERT" (Li et al., 2020)](https://arxiv.org/abs/2004.09984))</sub></td>
+<td ><sub> <a href="https://arxiv.org/abs/2004.09984">"BERT-ATTACK: Adversarial Attack Against BERT Using BERT" (Li et al., 2020)</a></sub></td>
 </tr>
 <tr>
 <td><code>checklist</code> <span class="citation" data-cites="Gao2018BlackBoxGO"></span></td>
 <td><sub>{Untargeted, Targeted} Classification</sub></td>
 <td><sub>checklist distance</sub></td>
 <td><sub>contract, extend, and substitutes name entities</sub></td>
 <td><sub>Greedy-WIR</sub></td>
-<td ><sub>Invariance testing implemented in CheckList . (["Beyond Accuracy: Behavioral Testing of NLP models with CheckList" (Ribeiro et al., 2020)](https://arxiv.org/abs/2005.04118))</sub></td>
+<td ><sub>Invariance testing implemented in CheckList. <a href="https://arxiv.org/abs/2005.04118">"Beyond Accuracy: Behavioral Testing of NLP models with CheckList" (Ribeiro et al., 2020)</a></sub></td>
 </tr>
 <tr>
 <td> <code>clare</code> <span class="citation" data-cites="Alzantot2018GeneratingNL Jia2019CertifiedRT"></span></td>
 <td><sub>Untargeted {Classification, Entailment}</sub></td>
 <td><sub>USE sentence encoding cosine similarity</sub></td>
 <td><sub>RoBERTa Masked Prediction for token swap, insert and merge</sub></td>
 <td><sub>Greedy</sub></td>
-<td ><sub>["Contextualized Perturbation for Textual Adversarial Attack" (Li et al., 2020)](https://arxiv.org/abs/2009.07502))</sub></td>
+<td ><sub><a href="https://arxiv.org/abs/2009.07502">"Contextualized Perturbation for Textual Adversarial Attack" (Li et al., 2020)</a></sub></td>
 </tr>
 <tr>
 <td><code>deepwordbug</code> <span class="citation" data-cites="Gao2018BlackBoxGO"></span></td>
 <td><sub>{Untargeted, Targeted} Classification</sub></td>
 <td><sub>Levenshtein edit distance</sub></td>
 <td><sub>{Character Insertion, Character Deletion, Neighboring Character Swap, Character Substitution}</sub></td>
 <td><sub>Greedy-WIR</sub></td>
-<td ><sub>Greedy replace-1 scoring and multi-transformation character-swap attack (["Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers" (Gao et al., 2018)](https://arxiv.org/abs/1801.04354)</sub></td>
+<td ><sub>Greedy replace-1 scoring and multi-transformation character-swap attack, from <a href="https://arxiv.org/abs/1801.04354">"Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers" (Gao et al., 2018)</a></sub></td>
 </tr>
 <tr>
 <td> <code>faster-alzantot</code> <span class="citation" data-cites="Alzantot2018GeneratingNL Jia2019CertifiedRT"></span></td>
 <td><sub>Untargeted {Classification, Entailment}</sub></td>
 <td><sub>Percentage of words perturbed, Language Model perplexity, Word embedding distance</sub></td>
 <td><sub>Counter-fitted word embedding swap</sub></td>
 <td><sub>Genetic Algorithm</sub></td>
-<td ><sub>Modified, faster version of the Alzantot et al. genetic algorithm, from (["Certified Robustness to Adversarial Word Substitutions" (Jia et al., 2019)](https://arxiv.org/abs/1909.00986))</sub></td>
+<td ><sub>Modified, faster version of the Alzantot et al. genetic algorithm, from <a href="https://arxiv.org/abs/1909.00986">"Certified Robustness to Adversarial Word Substitutions" (Jia et al., 2019)</a></sub></td>
 </tr>
 <tr>
 <td><code>hotflip</code> (word swap) <span class="citation" data-cites="Ebrahimi2017HotFlipWA"></span></td>
 <td><sub>Untargeted Classification</sub></td>
 <td><sub>Word Embedding Cosine Similarity, Part-of-speech match, Number of words perturbed</sub></td>
 <td><sub>Gradient-Based Word Swap</sub></td>
 <td><sub>Beam search</sub></td>
-<td ><sub> (["HotFlip: White-Box Adversarial Examples for Text Classification" (Ebrahimi et al., 2017)](https://arxiv.org/abs/1712.06751))</sub></td>
+<td ><sub>from <a href="https://arxiv.org/abs/1712.06751">"HotFlip: White-Box Adversarial Examples for Text Classification" (Ebrahimi et al., 2017)</a></sub></td>
 </tr>
 <tr>
 <td><code>iga</code> <span class="citation" data-cites="iga-wang2019natural"></span></td>
 <td><sub>Untargeted {Classification, Entailment}</sub></td>
 <td><sub>Percentage of words perturbed, Word embedding distance</sub></td>
 <td><sub>Counter-fitted word embedding swap</sub></td>
 <td><sub>Genetic Algorithm</sub></td>
-<td ><sub>Improved genetic algorithm -based word substitution from (["Natural Language Adversarial Attacks and Defenses in Word Level (Wang et al., 2019)"](https://arxiv.org/abs/1909.06723)</sub></td>
+<td ><sub>Improved genetic algorithm -based word substitution, from <a href="https://arxiv.org/abs/1909.06723">"Natural Language Adversarial Attacks and Defenses in Word Level" (Wang et al., 2019)</a></sub></td>
 </tr>
 <tr>
 <td><code>input-reduction</code> <span class="citation" data-cites="feng2018pathologies"></span></td>
 <td><sub>Input Reduction</sub></td>
 <td></td>
 <td><sub>Word deletion</sub></td>
 <td><sub>Greedy-WIR</sub></td>
-<td ><sub>Greedy attack with word importance ranking , Reducing the input while maintaining the prediction through word importance ranking (["Pathologies of Neural Models Make Interpretation Difficult" (Feng et al., 2018)](https://arxiv.org/pdf/1804.07781.pdf))</sub></td>
+<td ><sub>Greedy attack with word importance ranking, reducing the input while maintaining the prediction through word importance ranking, from <a href="https://arxiv.org/pdf/1804.07781.pdf">"Pathologies of Neural Models Make Interpretation Difficult" (Feng et al., 2018)</a></sub></td>
 </tr>
 <tr>
 <td><code>kuleshov</code> <span class="citation" data-cites="Kuleshov2018AdversarialEF"></span></td>
 <td><sub>Untargeted Classification</sub></td>
 <td><sub>Thought vector encoding cosine similarity, Language model similarity probability</sub></td>
 <td><sub>Counter-fitted word embedding swap</sub></td>
 <td><sub>Greedy word swap</sub></td>
-<td ><sub>(["Adversarial Examples for Natural Language Classification Problems" (Kuleshov et al., 2018)](https://openreview.net/pdf?id=r1QZ3zbAZ)) </sub></td>
+<td ><sub>From <a href="https://openreview.net/pdf?id=r1QZ3zbAZ">"Adversarial Examples for Natural Language Classification Problems" (Kuleshov et al., 2018</a> </sub></td>
 </tr>
 <tr>
 <td><code>pruthi</code> <span class="citation" data-cites="pruthi2019combating"></span></td>
 <td><sub>Untargeted Classification</sub></td>
 <td><sub>Minimum word length, Maximum number of words perturbed</sub></td>
 <td><sub>{Neighboring Character Swap, Character Deletion, Character Insertion, Keyboard-Based Character Swap}</sub></td>
 <td><sub>Greedy search</sub></td>
-<td ><sub>simulates common typos (["Combating Adversarial Misspellings with Robust Word Recognition" (Pruthi et al., 2019)](https://arxiv.org/abs/1905.11268) </sub></td>
+<td ><sub>simulates common typos, from <a href="https://arxiv.org/abs/1905.11268">"Combating Adversarial Misspellings with Robust Word Recognition" (Pruthi et al., 2019)</a> </sub></td>
 </tr>
 <tr>
 <td><code>pso</code> <span class="citation" data-cites="pso-zang-etal-2020-word"></span></td>
 <td><sub>Untargeted Classification</sub></td>
 <td></td>
 <td><sub>HowNet Word Swap</sub></td>
 <td><sub>Particle Swarm Optimization</sub></td>
-<td ><sub>(["Word-level Textual Adversarial Attacking as Combinatorial Optimization" (Zang et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.540/)) </sub></td>
+<td ><sub>From <a href="https://www.aclweb.org/anthology/2020.acl-main.540/">"Word-level Textual Adversarial Attacking as Combinatorial Optimization" (Zang et al., 2020)</a></sub></td>
 </tr>
 <tr>
 <td><code>pwws</code> <span class="citation" data-cites="pwws-ren-etal-2019-generating"></span></td>
 <td><sub>Untargeted Classification</sub></td>
 <td></td>
 <td><sub>WordNet-based synonym swap</sub></td>
 <td><sub>Greedy-WIR (saliency)</sub></td>
-<td ><sub>Greedy attack with word importance ranking based on word saliency and synonym swap scores (["Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency" (Ren et al., 2019)](https://www.aclweb.org/anthology/P19-1103/))</sub> </td>
+<td ><sub>Greedy attack with word importance ranking based on word saliency and synonym swap scores, from <a href="https://www.aclweb.org/anthology/P19-1103/">"Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency" (Ren et al., 2019)</a></sub> </td>
 </tr>
 <tr>
 <td><code>textbugger</code> : (black-box) <span class="citation" data-cites="Li2019TextBuggerGA"></span></td>
 <td><sub>Untargeted Classification</sub></td>
 <td><sub>USE sentence encoding cosine similarity</sub></td>
 <td><sub>{Character Insertion, Character Deletion, Neighboring Character Swap, Character Substitution}</sub></td>
 <td><sub>Greedy-WIR</sub></td>
-<td ><sub>([(["TextBugger: Generating Adversarial Text Against Real-world Applications" (Li et al., 2018)](https://arxiv.org/abs/1812.05271)).</sub></td>
+<td ><sub>From <a href="https://arxiv.org/abs/1812.05271">"TextBugger: Generating Adversarial Text Against Real-world Applications" (Li et al., 2018)</a></sub></td>
 </tr>
 <tr>
 <td><code>textfooler</code> <span class="citation" data-cites="Jin2019TextFooler"></span></td>
 <td><sub>Untargeted {Classification, Entailment}</sub></td>
 <td><sub>Word Embedding Distance, Part-of-speech match, USE sentence encoding cosine similarity</sub></td>
 <td><sub>Counter-fitted word embedding swap</sub></td>
 <td><sub>Greedy-WIR</sub></td>
-<td ><sub>Greedy attack with word importance ranking  (["Is Bert Really Robust?" (Jin et al., 2019)](https://arxiv.org/abs/1907.11932))</sub> </td>
+<td ><sub>Greedy attack with word importance ranking, from <a href="https://arxiv.org/abs/1907.11932">"Is Bert Really Robust?" (Jin et al., 2019)</a></sub> </td>
 </tr>
 
 <tr><td style="text-align: center;" colspan="6"><strong><br>Attacks on sequence-to-sequence models: <br></strong></td></tr>
@@ -207,7 +207,7 @@ To run an attack recipe: `textattack attack --recipe [recipe_name]`
 <td></td>
 <td><sub>Inflection Word Swap</sub> </td>
 <td><sub>Greedy search</sub> </td>
-<td ><sub>Greedy to replace words with their inflections with the goal of minimizing BLEU score (["It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations"](https://www.aclweb.org/anthology/2020.acl-main.263.pdf)</sub> </td>
+<td ><sub>Greedy to replace words with their inflections with the goal of minimizing BLEU score, from <a href="https://www.aclweb.org/anthology/2020.acl-main.263.pdf">"It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations" (Tan et al., 2020)</a></sub> </td>
 </tr>
 
 </tr>
@@ -217,7 +217,7 @@ To run an attack recipe: `textattack attack --recipe [recipe_name]`
 <td></td>
 <td><sub>Counter-fitted word embedding swap</sub> </td>
 <td><sub>Greedy-WIR</sub></td>
-<td ><sub>Greedy attack with goal of changing every word in the output translation. Currently implemented as black-box with plans to change to white-box as done in paper (["Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples" (Cheng et al., 2018)](https://arxiv.org/abs/1803.01128)) </sub>  </td>
+<td ><sub>Greedy attack with goal of changing every word in the output translation. Currently implemented as black-box with plans to change to white-box as done in paper, from <a href="https://arxiv.org/abs/1803.01128">"Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples" (Cheng et al., 2018)</a></sub>  </td>
 </tr>