fix: correct reference length calculation (#195)

Summary:
This PR fixes the way brevity penalty (specifically the effective reference corpus length) is calculated in BLEU.

Previously, `len_reference` was calculated as `min([len(ref) for ref in references_tokenized])`. However, this is incorrect, because according to the paper, we need to find the "best match length", not the minimum reference length.

For more information, see [wikipedia - brevity penalty](https://en.wikipedia.org/wiki/BLEU#Brevity_penalty) and [nltk implementation](https://www.nltk.org/_modules/nltk/translate/bleu_score.html#closest_ref_length).

Pull Request resolved: #195

Test Plan: I added another unit test to `test_bleu.py` and compared the results of the calculations to the results of the `nltk.translate.bleu_score.corpus_bleu` function to make sure the implementation is correct.

Reviewed By: galrotem

Differential Revision: D56846091

Pulled By: JKSenthil

fbshipit-source-id: 2bf1cd0ba169535a118222e60f4264259248f1fd

tests/metrics/text/test_bleu.py

@@ -107,3 +107,32 @@ def test_bleu_multiple_examples_per_update(self) -> None:
             num_total_updates=2,
             num_processes=2,
         )
+
+    def test_bleu_brevity(self) -> None:
+        candidates = [["the squirrel is eating the nut"], ["the cat is on mat"]]
+        references = [
+            [
+                [
+                    "a squirrel is eating a nut",
+                    "the squirrel is eating a tasty nut",
+                    "hi",
+                ]
+            ],
+            [["there is a cat on the mat", "a cat is on the mat"]],
+        ]
+        self.run_class_implementation_tests(
+            metric=BLEUScore(n_gram=4),
+            state_names={
+                "input_len",
+                "target_len",
+                "matches_by_order",
+                "possible_matches_by_order",
+            },
+            update_kwargs={
+                "input": candidates,
+                "target": references,
+            },
+            compute_result=torch.tensor(0.41650065, dtype=torch.float64),
+            num_total_updates=2,
+            num_processes=2,
+        )

torcheval/metrics/functional/text/bleu.py

@@ -88,7 +88,10 @@ def _bleu_score_update(
         references_tokenized = [ref.split() for ref in references]
 
         len_candidate = len(candidate_tokenized)
-        len_reference = min([len(ref) for ref in references_tokenized])
+        len_reference = min(
+            [len(ref) for ref in references_tokenized],
+            key=lambda ref_len: (abs(ref_len - len_candidate), ref_len),
+        )
         input_len += len_candidate
         target_len += len_reference