WikiTableQuestions Dataset ========================== Version 1.0.2 (October 4, 2016) Introduction ------------ The WikiTableQuestions dataset is for the task of question answering on semi-structured HTML tables as presented in the paper: > Panupong Pasupat, Percy Liang. > [Compositional Semantic Parsing on Semi-Structured Tables](https://arxiv.org/abs/1508.00305) > Association for Computational Linguistics (ACL), 2015. More details about the project: TSV Format ---------- Many files in this dataset are stored as tab-separated values (TSV) with the following special constructs: - List items are separated by `|` (e.g., `when|was|taylor|swift|born|?`). - The following characters are escaped: newline (=> `\n`), backslash (`\` => `\\`), and pipe (`|` => `\p`) Note that pipes become `\p` so that doing `x.split('|')` will work. - Consecutive whitespaces (except newlines) are collapsed into a single space. Questions and Answers --------------------- The `data/` directory contains the questions, answers, and the ID of the tables that the questions are asking about. Each portion of the dataset is stored as a TSV file where each line contains one example. **Field descriptions:** - id: unique ID of the example - utterance: the question in its original format - context: the table used to answer the question - targetValue: the answer, possibly a `|`-separated list **Dataset Splits:** We split 22033 examples into multiple sets: - `training`: Training data (14152 examples) - `pristine-unseen-tables`: Test data -- the tables are *not seen* in training data (4344 examples) - `pristine-seen-tables`: Additional data where the tables are *seen* in training data. (3537 examples) (Initially intended to be used as development data, this portion of the dataset has not been used in any experiment in the paper.) - `random-split-*`: For development, we split `training.tsv` into random 80-20 splits. Within each split, tables in the training data (`random-split-seed-*-train`) and the test data (`random-split-seed-*-test`) are disjoint. - `training-before300`: The first 300 training examples. - `annotated-all.examples`: The first 300 training examples annotated with gold logical forms. For our ACL 2015 paper: - In development set experiments: we trained on `random-split-seed-{1,2,3}-train` and tested on `random-split-seed-{1,2,3}-test`, respectively. - In test set experiments: we trained on `training` and tested on `pristine-unseen-tables`. **Supplementary Files:** - `*.examples` files: The LispTree format of the dataset is used internally in our [SEMPRE](http://nlp.stanford.edu/software/sempre/) code base. The `*.examples` files contain the same information as the TSV files. Tables ------ The `csv/` directory contains the extracted tables, while the `page/` directory contains the raw HTML data of the whole web page. **Table Formats:** - `csv/xxx-csv/yyy.csv`: Comma-separated table (The first row is treated as the column header) The escaped characters include: double quote (`"` => `\"`) and backslash (`\` => `\\`). Newlines are represented as quoted line breaks. - `csv/xxx-csv/yyy.tsv`: Tab-separated table. The TSV escapes explained at the beginning are used. - `csv/xxx-csv/yyy.table`: Human-readable column-aligned table. Some information was loss during data conversion, so this format should not be used as an input. - `csv/xxx-csv/yyy.html`: Formatted HTML of just the table - `page/xxx-page/yyy.html`: Raw HTML of the whole web page - `page/xxx-page/yyy.json`: Metadata including the URL, the page title, and the index of the chosen table. (Only tables with the `wikitable` class are considered.) The conversion from HTML to CSV and TSV was done using `table-to-tsv.py`. Its dependency is in the `weblib/` directory. CoreNLP Tagged Files -------------------- Questions and tables are tagged using CoreNLP 3.5.2. The annotation is not perfect (e.g., it cannot detect the date "13-12-1989"), but it is usually good enough. - `tagged/data/*.tagged`: Tagged questions. Each line contains one example. Field descriptions: - id: unique ID of the example - utterance: the question in its original format - context: the table used to answer the question - targetValue: the answer, possibly a `|`-separated list - tokens: the question, tokenized - lemmaTokens: the question, tokenized and lemmatized - posTags: the part of speech tag of each token - nerTags: the name entity tag of each token - nerValues: if the NER tag is numerical or temporal, the value of that NER span will be listed here - targetCanon: canonical form of the answers where numbers and dates are converted into normalized values - targetCanonType: type of the canonical answers; possible values include "number", "date", "string", and "mixed" - `tagged/xxx-tagged/yyy.tagged`: Tab-separated file containing the CoreNLP annotation of each table cell. Each line represents one table cell. Mandatory fields: - row: row index (-1 is the header row) - col: column index - id: unique ID of the cell. - Each header cell gets a unique ID even when the contents are identical - Non-header cells get the same ID if they have exactly the same content - content: the cell text (images and hidden spans are removed) - tokens: the cell text, tokenized - lemmaTokens: the cell text, tokenized and lemmatized - posTags: the part of speech tag of each token - nerTags: the name entity tag of each token - nerValues: if the NER tag is numerical or temporal, the value of that NER span will be listed here The following fields are optional: - number: interpretation as a number (for multiple numbers, the first number is extracted) - date: interpretation as a date - num2: the second number in the cell (useful for scores like `1-2`) - list: interpretation as a list of items Header cells do not have these optional fields. Evaluator --------- `evaluator.py` is the official evaluator. Usage: evaluator.py - `tagged_dataset_path` should be a dataset .tagged file containing the relevant examples - `prediction_path` should contain predictions from the model. Each line should contain ex_id item1 item2 ... If the model does not produce a prediction, just output `ex_id` without the items. Note that the resulting scores will be different from what [SEMPRE](https://github.com/percyliang/sempre/) produces as SEMPRE also enforces the prediction to have the same type as the target value, while the official evaluator is more lenient. Version History --------------- 1.0 - Fixed various bugs in datasets (encoding issues, number normalization issues) 0.5 - Added evaluator 0.4 - Added annotated logical forms of the first 300 examples / Renamed CoreNLP tagged data as `tagged` to avoid confusion 0.3 - Repaired table headers / Added raw HTML tables / Added CoreNLP tagged data 0.2 - Initial release For questions and comments, please contact Ice Pasupat