[chat]: fix chat evaluation possible bug

applications/Chat/evaluate/metrics.py

@@ -141,8 +141,8 @@ def distinct_score(preds: List[str], language: str) -> Dict[str, float]:
             count_segs = len(pred_seg_list)
             unique_segs = set(pred_seg_list)
             count_unique_chars = len(unique_segs)
-
-            cumulative_distinct.append(count_unique_chars / count_segs)
+            # prevent denominator from being 0
+            cumulative_distinct.append(count_unique_chars / (count_segs + 1e-6))
         elif language == "en":
             # calculate distinct 1-gram, 2-gram, 3-gram
             unique_ngram = [set() for _ in range(0, 3)]

applications/Chat/evaluate/unieval/evaluator.py

@@ -80,7 +80,8 @@ def evaluate(self, data, category, dims=None, overall=True):
                 start_idx = 0
                 score = []
                 for cur_n_sent in n_sents:
-                    score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / cur_n_sent)
+                    # prevent denominator from being 0
+                    score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / (cur_n_sent + 1e-6))
                     start_idx += cur_n_sent
 
             # Calculate summary-level score for 'coherence' and 'relevance'

applications/Chat/evaluate/utils.py

@@ -72,17 +72,6 @@ def get_data_per_category(data, categories):
     return data_per_category
 
 
-def remove_articles(text: str) -> str:
-    """
-    Remove articles "a, an, the" in the given text.
-    It is used in evaluation of automatic metrics.
-
-    """
-
-    pattern = re.compile(r"\b(a|an|the)\b", re.UNICODE)
-    return re.sub(pattern, " ", text)
-
-
 def remove_punctuations(text: str) -> str:
     """
     Remove punctuations in the given text.
@@ -121,7 +110,7 @@ def preprocessing_text(text: str) -> str:
 
     """
 
-    return remove_redundant_space(remove_articles(remove_punctuations(text.lower())))
+    return remove_redundant_space(remove_punctuations(text.lower()))
 
 
 def save_automatic_results(model_name: str, automatic_metric_stats: Dict[str, Dict], save_path: str) -> None: