Fixed the issue where token IDs were not converted to word-piece IDs …

…for BERT value linking. Closes #4.

README.md

@@ -21,6 +21,7 @@ If you use RAT-SQL in your work, please cite it as follows:
 **2020-08-14:**
 - The Docker image now inherits from a CUDA-enabled base image.
 - Clarified memory and dataset requirements on the image.
+- Fixed the issue where token IDs were not converted to word-piece IDs for BERT value linking.  
 
 ## Usage
 

ratsql/datasets/spider.py

@@ -150,7 +150,7 @@ def __init__(self, paths, tables_paths, db_path, demo_path=None, limit=None):
         for db_id, schema in tqdm(self.schemas.items(), desc="DB connections"):
             sqlite_path = Path(db_path) / db_id / f"{db_id}.sqlite"
             source: sqlite3.Connection
-            with sqlite3.connect(sqlite_path) as source:
+            with sqlite3.connect(str(sqlite_path)) as source:
                 dest = sqlite3.connect(':memory:')
                 dest.row_factory = sqlite3.Row
                 source.backup(dest)

ratsql/models/spider/spider_enc.py

@@ -546,6 +546,7 @@ def __init__(self, pieces):
         self.pieces = pieces
 
         self.normalized_pieces = None
+        self.recovered_pieces = None
         self.idx_map = None
 
         self.normalize_toks()
@@ -605,6 +606,7 @@ def normalize_toks(self):
             normalized_toks.append(lemma_word)
 
         self.normalized_pieces = normalized_toks
+        self.recovered_pieces = new_toks
 
     def bert_schema_linking(self, columns, tables):
         question_tokens = self.normalized_pieces
@@ -624,6 +626,21 @@ def bert_schema_linking(self, columns, tables):
             new_sc_link[m_type] = _match
         return new_sc_link
 
+    def bert_cv_linking(self, schema):
+        question_tokens = self.recovered_pieces  # Not using normalized tokens here because values usually match exactly
+        cv_link = compute_cell_value_linking(question_tokens, schema)
+
+        new_cv_link = {}
+        for m_type in cv_link:
+            _match = {}
+            for ij_str in cv_link[m_type]:
+                q_id_str, col_tab_id_str = ij_str.split(",")
+                q_id, col_tab_id = int(q_id_str), int(col_tab_id_str)
+                real_q_id = self.idx_map[q_id]
+                _match[f"{real_q_id},{col_tab_id}"] = cv_link[m_type][ij_str]
+            new_cv_link[m_type] = _match
+        return new_cv_link
+
 
 class SpiderEncoderBertPreproc(SpiderEncoderV2Preproc):
 
@@ -667,8 +684,8 @@ def add_item(self, item, section, validation_info):
     def preprocess_item(self, item, validation_info):
         question = self._tokenize(item.text, item.orig['question'])
         preproc_schema = self._preprocess_schema(item.schema)
+        question_bert_tokens = Bertokens(question)
         if self.compute_sc_link:
-            question_bert_tokens = Bertokens(question)
             sc_link = question_bert_tokens.bert_schema_linking(
                 preproc_schema.normalized_column_names,
                 preproc_schema.normalized_table_names
@@ -677,8 +694,7 @@ def preprocess_item(self, item, validation_info):
             sc_link = {"q_col_match": {}, "q_tab_match": {}}
 
         if self.compute_cv_link:
-            question_bert_tokens = Bertokens(question)
-            cv_link = compute_cell_value_linking(question_bert_tokens.normalized_pieces, item.schema)
+            cv_link = question_bert_tokens.bert_cv_linking(item.schema)
         else:
             cv_link = {"num_date_match": {}, "cell_match": {}}