linearization

dataset_infos.json

@@ -1 +1 @@
-{"totto": {"description": "ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. \n", "citation": "\\@inproceedings{parikh2020totto,\ntitle={{ToTTo}: A Controlled Table-To-Text Generation Dataset},\nauthor={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},\nbooktitle={Proceedings of EMNLP},\nyear={2020}\n}\n", "homepage": "", "license": "", "features": {"gem_id": {"dtype": "string", "id": null, "_type": "Value"}, "gem_parent_id": {"dtype": "string", "id": null, "_type": "Value"}, "totto_id": {"dtype": "int32", "id": null, "_type": "Value"}, "table_page_title": {"dtype": "string", "id": null, "_type": "Value"}, "table_webpage_url": {"dtype": "string", "id": null, "_type": "Value"}, "table_section_title": {"dtype": "string", "id": null, "_type": "Value"}, "table_section_text": {"dtype": "string", "id": null, "_type": "Value"}, "table": [[{"column_span": {"dtype": "int32", "id": null, "_type": "Value"}, "is_header": {"dtype": "bool", "id": null, "_type": "Value"}, "row_span": {"dtype": "int32", "id": null, "_type": "Value"}, "value": {"dtype": "string", "id": null, "_type": "Value"}}]], "highlighted_cells": [[{"dtype": "int32", "id": null, "_type": "Value"}]], "example_id": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_annotations": [{"original_sentence": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_after_deletion": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_after_ambiguity": {"dtype": "string", "id": null, "_type": "Value"}, "final_sentence": {"dtype": "string", "id": null, "_type": "Value"}}], "overlap_subset": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"dtype": "string", "id": null, "_type": "Value"}, "references": [{"dtype": "string", "id": null, "_type": "Value"}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "mlsum", "config_name": "totto", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 669655077, "num_examples": 121153, "dataset_name": "mlsum"}, "validation": {"name": "validation", "num_bytes": 50334466, "num_examples": 7700, "dataset_name": "mlsum"}, "test": {"name": "test", "num_bytes": 40896774, "num_examples": 7700, "dataset_name": "mlsum"}, "challenge_train_sample": {"name": "challenge_train_sample", "num_bytes": 2262167, "num_examples": 500, "dataset_name": "mlsum"}, "challenge_validation_sample": {"name": "challenge_validation_sample", "num_bytes": 3371787, "num_examples": 500, "dataset_name": "mlsum"}, "challenge_test_scramble": {"name": "challenge_test_scramble", "num_bytes": 2612484, "num_examples": 500, "dataset_name": "mlsum"}}, "download_checksums": {"https://storage.googleapis.com/totto-public/totto_data.zip": {"num_bytes": 187724372, "checksum": "0aab72597057394514fd9659745fd2b318d1a64bf0b2ca1b2c339abe0692fdf2"}, "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/totto.zip": {"num_bytes": 1810237, "checksum": "86bcec978edc44caa7a313944cecaaeb52e4685ee05f7be073911a15665d5ac3"}}, "download_size": 189534609, "post_processing_size": null, "dataset_size": 769132755, "size_in_bytes": 958667364}}
+{"totto": {"description": "ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.\n", "citation": "\\@inproceedings{parikh2020totto,\ntitle={{ToTTo}: A Controlled Table-To-Text Generation Dataset},\nauthor={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},\nbooktitle={Proceedings of EMNLP},\nyear={2020}\n}\n", "homepage": "", "license": "", "features": {"gem_id": {"dtype": "string", "id": null, "_type": "Value"}, "gem_parent_id": {"dtype": "string", "id": null, "_type": "Value"}, "totto_id": {"dtype": "int32", "id": null, "_type": "Value"}, "table_page_title": {"dtype": "string", "id": null, "_type": "Value"}, "table_webpage_url": {"dtype": "string", "id": null, "_type": "Value"}, "table_section_title": {"dtype": "string", "id": null, "_type": "Value"}, "table_section_text": {"dtype": "string", "id": null, "_type": "Value"}, "table": [[{"column_span": {"dtype": "int32", "id": null, "_type": "Value"}, "is_header": {"dtype": "bool", "id": null, "_type": "Value"}, "row_span": {"dtype": "int32", "id": null, "_type": "Value"}, "value": {"dtype": "string", "id": null, "_type": "Value"}}]], "highlighted_cells": [[{"dtype": "int32", "id": null, "_type": "Value"}]], "example_id": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_annotations": [{"original_sentence": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_after_deletion": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_after_ambiguity": {"dtype": "string", "id": null, "_type": "Value"}, "final_sentence": {"dtype": "string", "id": null, "_type": "Value"}}], "overlap_subset": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"dtype": "string", "id": null, "_type": "Value"}, "references": [{"dtype": "string", "id": null, "_type": "Value"}], "linearized_input": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "totto", "config_name": "totto", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 747884464, "num_examples": 121153, "dataset_name": "totto"}, "validation": {"name": "validation", "num_bytes": 54580701, "num_examples": 7700, "dataset_name": "totto"}, "test": {"name": "test", "num_bytes": 45476943, "num_examples": 7700, "dataset_name": "totto"}, "challenge_train_sample": {"name": "challenge_train_sample", "num_bytes": 2521208, "num_examples": 500, "dataset_name": "totto"}, "challenge_validation_sample": {"name": "challenge_validation_sample", "num_bytes": 3693554, "num_examples": 500, "dataset_name": "totto"}}, "download_checksums": {"https://storage.googleapis.com/totto-public/totto_data.zip": {"num_bytes": 187724372, "checksum": "0aab72597057394514fd9659745fd2b318d1a64bf0b2ca1b2c339abe0692fdf2"}, "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/totto.zip": {"num_bytes": 1810237, "checksum": "86bcec978edc44caa7a313944cecaaeb52e4685ee05f7be073911a15665d5ac3"}}, "download_size": 189534609, "post_processing_size": null, "dataset_size": 854156870, "size_in_bytes": 1043691479}}

totto.py

@@ -1,3 +1,4 @@
+import copy
 import json
 import os
 
@@ -12,7 +13,7 @@ year={2020}
 """
 
 _DESCRIPTION = """\
-ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. 
+ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
 """
 
 _URLs = {
@@ -23,7 +24,132 @@ _URLs = {
 }
 
 
-class Mlsum(datasets.GeneratorBasedBuilder):
+def _add_adjusted_col_offsets(table):
+    """Add adjusted column offsets to take into account multi-column cells."""
+    adjusted_table = []
+    for row in table:
+        real_col_index = 0
+        adjusted_row = []
+        for cell in row:
+            adjusted_cell = copy.deepcopy(cell)
+            adjusted_cell["adjusted_col_start"] = real_col_index
+            adjusted_cell["adjusted_col_end"] = (
+                adjusted_cell["adjusted_col_start"] + adjusted_cell["column_span"]
+            )
+            real_col_index += adjusted_cell["column_span"]
+            adjusted_row.append(adjusted_cell)
+        adjusted_table.append(adjusted_row)
+    return adjusted_table
+
+
+def _get_heuristic_row_headers(adjusted_table, row_index, col_index):
+    """Heuristic to find row headers."""
+    row_headers = []
+    row = adjusted_table[row_index]
+    for i in range(0, col_index):
+        if row[i]["is_header"]:
+            row_headers.append(row[i])
+    return row_headers
+
+
+def _get_heuristic_col_headers(adjusted_table, row_index, col_index):
+    """Heuristic to find column headers."""
+    adjusted_cell = adjusted_table[row_index][col_index]
+    adjusted_col_start = adjusted_cell["adjusted_col_start"]
+    adjusted_col_end = adjusted_cell["adjusted_col_end"]
+    col_headers = []
+    for r in range(0, row_index):
+        row = adjusted_table[r]
+        for cell in row:
+            if (
+                cell["adjusted_col_start"] < adjusted_col_end
+                and cell["adjusted_col_end"] > adjusted_col_start
+            ):
+                if cell["is_header"]:
+                    col_headers.append(cell)
+
+    return col_headers
+
+
+def get_highlighted_subtable(table, cell_indices, with_heuristic_headers=False):
+    """Extract out the highlighted part of a table."""
+    highlighted_table = []
+
+    adjusted_table = _add_adjusted_col_offsets(table)
+
+    for (row_index, col_index) in cell_indices:
+        cell = table[row_index][col_index]
+        if with_heuristic_headers:
+            row_headers = _get_heuristic_row_headers(
+                adjusted_table, row_index, col_index
+            )
+            col_headers = _get_heuristic_col_headers(
+                adjusted_table, row_index, col_index
+            )
+        else:
+            row_headers = []
+            col_headers = []
+
+        highlighted_cell = {
+            "cell": cell,
+            "row_headers": row_headers,
+            "col_headers": col_headers,
+        }
+        highlighted_table.append(highlighted_cell)
+
+    return highlighted_table
+
+
+def linearize_subtable(subtable, table_page_title, table_section_title):
+    """Linearize the highlighted subtable and return a string of its contents."""
+    table_str = ""
+    if table_page_title:
+        table_str += "<page_title> " + table_page_title + " </page_title> "
+    if table_section_title:
+        table_str += "<section_title> " + table_section_title + " </section_title> "
+    table_str += "<table> "
+
+    for item in subtable:
+        cell = item["cell"]
+        row_headers = item["row_headers"]
+        col_headers = item["col_headers"]
+
+        # The value of the cell.
+        item_str = "<cell> " + cell["value"] + " "
+
+        # All the column headers associated with this cell.
+        for col_header in col_headers:
+            item_str += "<col_header> " + col_header["value"] + " </col_header> "
+
+        # All the row headers associated with this cell.
+        for row_header in row_headers:
+            item_str += "<row_header> " + row_header["value"] + " </row_header> "
+
+        item_str += "</cell> "
+        table_str += item_str
+
+    table_str += "</table>"
+    return table_str
+
+
+def linearize(example):
+    table = example["table"]
+    table_page_title = example["table_page_title"]
+    table_section_title = example["table_section_title"]
+    cell_indices = example["highlighted_cells"]
+    subtable = get_highlighted_subtable(
+        table=table, cell_indices=cell_indices, with_heuristic_headers=True
+    )
+
+    subtable_metadata_str = linearize_subtable(
+        subtable=subtable,
+        table_page_title=table_page_title,
+        table_section_title=table_section_title,
+    )
+    return subtable_metadata_str
+
+
+class Totto(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
@@ -36,7 +162,7 @@ class Mlsum(datasets.GeneratorBasedBuilder):
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            features = datasets.Features(
+            features=datasets.Features(
                 {
                     "gem_id": datasets.Value("string"),
                     "gem_parent_id": datasets.Value("string"),
@@ -68,6 +194,7 @@ class Mlsum(datasets.GeneratorBasedBuilder):
                     "overlap_subset": datasets.Value("string"),
                     "target": datasets.Value("string"),  # single target for train
                     "references": [datasets.Value("string")],
+                    "linearized_input": datasets.Value("string"),
                 },
             ),
             supervised_keys=None,
@@ -79,43 +206,51 @@ class Mlsum(datasets.GeneratorBasedBuilder):
         """Returns SplitGenerators."""
         dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
         challenge_sets = [
-                ("challenge_train_sample", "train_totto_RandomSample500.json"),
-                ("challenge_validation_sample", "validation_totto_RandomSample500.json"),
-                ("challenge_test_scramble", "test_totto_ScrambleInputStructure500.json"),
-            ]
+            ("challenge_train_sample", "train_totto_RandomSample500.json"),
+            ("challenge_validation_sample", "validation_totto_RandomSample500.json"),
+            # ("challenge_test_scramble", "test_totto_ScrambleInputStructure500.json"),
+        ]
 
         return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepath": os.path.join(dl_dir["data"], "totto_data/totto_train_data.jsonl"),
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={
-                        "filepath": os.path.join(dl_dir["data"], "totto_data/totto_dev_data.jsonl"),
-                        "split": "validation",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={
-                        "filepath": os.path.join(dl_dir["data"], "totto_data/unlabeled_totto_test_data.jsonl"),
-                        "split": "test",
-                    },
-                ),
-            ] + [
-                datasets.SplitGenerator(
-                    name=challenge_split,
-                    gen_kwargs={
-                        "filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename),
-                        "split": challenge_split,
-                    },
-                )
-                for challenge_split, filename in challenge_sets
-            ]
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        dl_dir["data"], "totto_data/totto_train_data.jsonl"
+                    ),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        dl_dir["data"], "totto_data/totto_dev_data.jsonl"
+                    ),
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        dl_dir["data"], "totto_data/unlabeled_totto_test_data.jsonl"
+                    ),
+                    "split": "test",
+                },
+            ),
+        ] + [
+            datasets.SplitGenerator(
+                name=challenge_split,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        dl_dir["challenge_set"], self.config.name, filename
+                    ),
+                    "split": challenge_split,
+                },
+            )
+            for challenge_split, filename in challenge_sets
+        ]
 
     def _generate_examples(self, filepath, split, filepaths=None, lang=None):
         """Yields examples."""
@@ -129,6 +264,7 @@ class Mlsum(datasets.GeneratorBasedBuilder):
                     continue
                 exple["gem_parent_id"] = exple["gem_id"]
                 exple["gem_id"] = f"{self.config.name}-{split}-{id_}"
+                exple["linearized_input"] = linearize(exple)
                 yield id_, exple
         else:
             with open(filepath, "r", encoding="utf-8") as json_file:
@@ -137,6 +273,7 @@ class Mlsum(datasets.GeneratorBasedBuilder):
                 i = -1
                 for json_str in json_list:
                     result = json.loads(json_str)
+                    linearized_input = linearize(result)
                     if split == "train":
                         i += 1
                         for sentence in result["sentence_annotations"]:
@@ -154,8 +291,9 @@ class Mlsum(datasets.GeneratorBasedBuilder):
                                 "example_id": str(result["example_id"]),
                                 "overlap_subset": "none",
                                 "sentence_annotations": [sentence],
-                                "references": [],
+                                "references": [sentence["final_sentence"]],
                                 "target": sentence["final_sentence"],
+                                "linearized_input": linearized_input,
                             }
                             yield id_, response
                     else:
@@ -172,11 +310,18 @@ class Mlsum(datasets.GeneratorBasedBuilder):
                             "highlighted_cells": result["highlighted_cells"],
                             "example_id": str(result["example_id"]),
                             "overlap_subset": str(result["overlap_subset"]),
+                            "linearized_input": linearized_input,
                         }
-                        response["sentence_annotations"] = [] if split == "test" else result["sentence_annotations"]
+                        response["sentence_annotations"] = (
+                            [] if split == "test" else result["sentence_annotations"]
+                        )
                         response["references"] = [
-                            sentence["final_sentence"] for sentence in response["sentence_annotations"]
+                            sentence["final_sentence"]
+                            for sentence in response["sentence_annotations"]
                         ]
-                        response["target"] = response["references"][0] if len(response["references"]) > 0 else ""
+                        response["target"] = (
+                            response["references"][0]
+                            if len(response["references"]) > 0
+                            else ""
+                        )
                         yield id_, response
-