| 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: <https://nlp.stanford.edu/software/sempre/wikitable/> | |
| 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> <prediction_path> | |
| - `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 <tab> item1 <tab> item2 <tab> ... | |
| 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 <[email protected]> | |