[CODE] Add new attack recipe A2T

README.md

@@ -128,6 +128,15 @@ To run an attack recipe: `textattack attack --recipe [recipe_name]`
 <tbody>
   <tr><td style="text-align: center;" colspan="6"><strong><br>Attacks on classification tasks, like sentiment classification and entailment:<br></strong></td></tr>
 
+<tr>
+<td><code>a2t</code> 
+<span class="citation" data-cites="yoo2021a2t"></span></td>
+<td><sub>Untargeted {Classification, Entailment}</sub></td>
+<td><sub>Percentage of words perturbed, Word embedding distance, DistilBERT sentence encoding cosine similarity, part-of-speech consistency</sub></td>
+<td><sub>Counter-fitted word embedding swap (or) BERT Masked Token Prediction</sub></td>
+<td><sub>Greedy-WIR (gradient)</sub></td>
+<td ><sub>from (["Towards Improving Adversarial Training of NLP Models" (Yoo et al., 2021)](https://arxiv.org/abs/2109.00544))</sub></td>
+</tr>
 <tr>
 <td><code>alzantot</code>  <span class="citation" data-cites="Alzantot2018GeneratingNL Jia2019CertifiedRT"></span></td>
 <td><sub>Untargeted {Classification, Entailment}</sub></td>

textattack/attack_args.py

@@ -35,6 +35,7 @@ ATTACK_RECIPE_NAMES = {
     "pso": "textattack.attack_recipes.PSOZang2020",
     "checklist": "textattack.attack_recipes.CheckList2020",
     "clare": "textattack.attack_recipes.CLARE2020",
+    "a2t": "textattack.attack_recipes.A2TYoo2021",
 }
 
 

textattack/attack_recipes/__init__.py

@@ -20,6 +20,7 @@ TextAttack supports the following attack recipes (each recipe's documentation co
 
 from .attack_recipe import AttackRecipe
 
+from .a2t_yoo_2021 import A2TYoo2021
 from .bae_garg_2019 import BAEGarg2019
 from .bert_attack_li_2020 import BERTAttackLi2020
 from .genetic_algorithm_alzantot_2018 import GeneticAlgorithmAlzantot2018

textattack/attack_recipes/a2t_yoo_2021.py

@@ -0,0 +1,68 @@
+from textattack import Attack
+from textattack.constraints.grammaticality import PartOfSpeech
+from textattack.constraints.pre_transformation import (
+    InputColumnModification,
+    MaxModificationRate,
+    RepeatModification,
+    StopwordModification,
+)
+from textattack.constraints.semantics import WordEmbeddingDistance
+from textattack.constraints.semantics.sentence_encoders import BERT
+from textattack.goal_functions import UntargetedClassification
+from textattack.search_methods import GreedyWordSwapWIR
+from textattack.transformations import WordSwapEmbedding, WordSwapMaskedLM
+
+from .attack_recipe import AttackRecipe
+
+
+class A2TYoo2021(AttackRecipe):
+    """Towards Improving Adversarial Training of NLP Models.
+
+    (Yoo et al., 2021)
+
+    https://arxiv.org/abs/2109.00544
+    """
+
+    @staticmethod
+    def build(model_wrapper, mlm=False):
+        """Build attack recipe.
+
+        Args:
+            model_wrapper (:class:`~textattack.models.wrappers.ModelWrapper`):
+                Model wrapper containing both the model and the tokenizer.
+            mlm (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                If :obj:`True`, load `A2T-MLM` attack. Otherwise, load regular `A2T` attack.
+
+        Returns:
+            :class:`~textattack.Attack`: A2T attack.
+        """
+        constraints = [RepeatModification(), StopwordModification()]
+        input_column_modification = InputColumnModification(
+            ["premise", "hypothesis"], {"premise"}
+        )
+        constraints.append(input_column_modification)
+        constraints.append(PartOfSpeech(allow_verb_noun_swap=False))
+        constraints.append(MaxModificationRate(max_rate=0.1, min_threshold=4))
+        sent_encoder = BERT(
+            model_name="stsb-distilbert-base", threshold=0.9, metric="cosine"
+        )
+        constraints.append(sent_encoder)
+
+        if mlm:
+            transformation = transformation = WordSwapMaskedLM(
+                method="bae", max_candidates=20, min_confidence=0.0, batch_size=16
+            )
+        else:
+            transformation = WordSwapEmbedding(max_candidates=20)
+            constraints.append(WordEmbeddingDistance(min_cos_sim=0.8))
+
+        #
+        # Goal is untargeted classification
+        #
+        goal_function = UntargetedClassification(model_wrapper, model_batch_size=32)
+        #
+        # Greedily swap words with "Word Importance Ranking".
+        #
+        search_method = GreedyWordSwapWIR(wir_method="gradient")
+
+        return Attack(goal_function, constraints, transformation, search_method)