diff --git a/main.py b/main.py
index 6b5d075..ea6a329 100644
--- a/main.py
+++ b/main.py
@@ -41,7 +41,8 @@ def main(args):
         embedder_list.append(museEmbedder)
 
     if args.wce_embedder:
-        wceEmbedder = WordClassGen(n_jobs=args.n_jobs)
+        wceEmbedder = WordClassGen(n_jobs=args.n_jobs,
+                                   zero_shot=zero_shot, train_langs=zscl_train_langs)   # Todo: testing zero shot
         embedder_list.append(wceEmbedder)
 
     if args.gru_embedder:
@@ -74,7 +75,7 @@ def main(args):
     # Testing ----------------------------------------
     print('\n[Testing Generalized Funnelling]')
     time_te = time.time()
-    # Zero shot scenario -> setting first tier learners zero_shot param to False
+    # TODO: Zero shot scenario -> setting first tier learners zero_shot param to False
     gfun.set_zero_shot(val=False)
     ly_ = gfun.predict(lXte)
     l_eval = evaluate(ly_true=lyte, ly_pred=ly_)
diff --git a/src/view_generators.py b/src/view_generators.py
index 19110c2..688f133 100644
--- a/src/view_generators.py
+++ b/src/view_generators.py
@@ -181,7 +181,7 @@ class WordClassGen(ViewGen):
     View Generator (w): generates document representation via Word-Class-Embeddings.
     Document embeddings are obtained via weighted sum of document's constituent embeddings.
     """
-    def __init__(self, n_jobs=-1):
+    def __init__(self, zero_shot=False, train_langs: list = None, n_jobs=-1):
         """
         Init WordClassGen.
         :param n_jobs: int, number of concurrent workers
@@ -191,6 +191,11 @@ class WordClassGen(ViewGen):
         self.langs = None
         self.lWce = None
         self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
+        # Zero shot parameters
+        self.zero_shot = zero_shot
+        if train_langs is None:
+            train_langs = ['it']
+        self.train_langs = train_langs
 
     def fit(self, lX, ly):
         """
@@ -215,19 +220,34 @@ class WordClassGen(ViewGen):
         :param lX: dict {lang: indexed documents}
         :return: document projection to the common latent space.
         """
-        lX = self.vectorizer.transform(lX)
+        # Testing zero-shot experiments
+        if self.zero_shot:
+            lX = self.zero_shot_experiments(lX)
+            lX = {l: self.vectorizer.vectorizer[l].transform(lX[l]) for l in self.langs if lX[l] is not None}
+        else:
+            lX = self.vectorizer.transform(lX)
         XdotWce = Parallel(n_jobs=self.n_jobs)(
-            delayed(XdotM)(lX[lang], self.lWce[lang], sif=True) for lang in self.langs)
-        lWce = {l: XdotWce[i] for i, l in enumerate(self.langs)}
+            delayed(XdotM)(lX[lang], self.lWce[lang], sif=True) for lang in sorted(lX.keys()))
+        lWce = {l: XdotWce[i] for i, l in enumerate(sorted(lX.keys()))}
         lWce = _normalize(lWce, l2=True)
         return lWce
 
     def fit_transform(self, lX, ly):
         return self.fit(lX, ly).transform(lX)
 
+    def zero_shot_experiments(self, lX):
+        print(f'# Zero-shot setting! Training langs will be set to: {sorted(self.train_langs)}')
+        _lX = {}
+        for lang in self.langs:
+            if lang in self.train_langs:
+                _lX[lang] = lX[lang]
+            else:
+                _lX[lang] = None
+        lX = _lX
+        return lX
+
     def set_zero_shot(self, val: bool):
         self.zero_shot = val
-        print('# TODO: WordClassGen has not been configured for zero-shot experiments')
         return