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NBA Game Dataset for Text Content Manipulation

This is a dataset for the task of text content manipulation, as first proposed in the paper:

Toward Unsupervised Text Content Manipulation
Wentao Wang*, Zhiting Hu*, Zichao Yang, Haoran Shi, Frank Xu, Eric P. Xing; 2019

Data Format

Each example in the dataset consists of four elements, namely, (x, y_aux, x_ref, y_ref), where

  • x is a content record containing a set of data tuples x = {x_i}. Each tuple x_i contains three fields (type, value, associated). For example, x_i = (TEAM-AST, 25, Boston) means "The Boston got 25 team assists". More specifically,

    • type: data type of the tuple, e.g., TEAM-AST, PLAYER-PTS, etc. There are 34 data types in total. See the file x_type.vocab.txt for all data types.
    • value: value of the data. Usually a scalar number or a string (e.g., a player's name).
    • associated: the associated team or player of the tuple.

    The above three fields of each x instance are stored in three parallel files, respectively. For example, each line in the file train/x_type.train.txt contains data types of all tuples in each x training instance. Data types are separated by white spaces. For example, the first line in train/x_type.train.txt is TEAM_NAME TEAM-AST TEAM-AST TEAM_NAME, meaning that there are 4 tuples in the first x instance, each of which has the respective type.

    We also provide joined files of x. For example, each line in train/x.joined.train.txt contains all tuples in each x training instance. In each tuple, the three fields are joined, separated by |. For example, the first line in train/x.joined.train.txt is Boston|TEAM_NAME|Boston 25|TEAM-AST|Boston 11|TEAM-AST|New_York New_York|TEAM_NAME|New_York. These files are simply joined from the separated files, and only used when evaluating the results.

  • y_aux is the auxiliary sentence describing the content of x.

  • x_ref is the content record of reference sentence y_ref, in the same format as x. During data construction, we have guaranteed x_ref has a similar structure with x, but has a different number of tuples or has different values or types.

  • y_ref is the reference sentence that defines the desired writing style of output sentence.

Data Files

  • The dataset is split into train/val/test sets, each in corresponding folder, respectively.

  • The four elements (x, y_aux, x_ref, y_ref) of each example are stored in parallel files, respectively. For example, each line of train/y_aux.train.txt is an auxiliary sentence of the respective data example.

    As explained above, three fields of x are separately stored in three files, namely, (taking training data for example), x_type.train.txt, x_value.train.txt, and x_associated.train.txt, respectively. Also, joined tuples of x are stored in a single file, namely, (again taking training data for example), x.joined.train.txt. x_ref is stored in the same format, in files like x_ref_type.train.txt or x_ref.joined.train.txt.

  • The vocabulary file y.vocab.txt contains all words that have occurred in y_aux and y_ref. x_type.vocab.txt, x_value.vocab.txt, and x_associated.vocab.txt are the vocabulary of the 'type', 'value', and 'associated' fields of both x and x_ref.

Data Statistics

train valid test
#Instances 31,751 6,833 6,999
#Tokens 1.64M 0.35M 0.36M
Avg Sentence Length 25.90 25.87 25.99
#Data Types 34 34 34
Avg Record Length 4.88 4.88 4.94

Dataset Creation Process

We briefly describe the process of creating the above dataset.

This dataset is derived from one of the Data-to-Text Datasets (RotoWire) proposed in the paper (Wiseman et al., 2017) Challenges in Data-to-Document Generation, which is for NBA game report generation. The original data can be downloaded from here.

The original dataset consists of (table, paragraph) pairs. We first split each data example into (record, sentence) pairs:

  • The original dataset is then preprocessed with a modified version of the script provided in the Data-to-Text dataset. In this step, we make sure each name of an entity (team/city/player) become a single token (e.g., LeBron_James, Los_Angeles_Clippers), and all numbers are replaced by their digital forms (e.g., if the original text is fifty, we replace it with 50).

  • We split the paragraph in each data example into sentences, i.e., the y_aux.

  • We then use the above script to extract all candidate relations between entities and numbers in each sentence y_aux. More rule-based constraints are imposed to filter out as many redundant relations as possible. These extracted relations forms the record x. So far, we have obtained all (x, y_aux) pairs.

We next use a retrieval method to retrieve from the training set a (x_ref, y_ref) pair for each of the above (x, y_aux) pairs. In particular, as mentioned above, we want to guarantee x_ref has a similar but not exact the same content with x. Formally, we use the following criteria for retrieval:

where types(x) is the set of all data types in record x; J(A, B) is the Jaccard index between two sets A and B. The larger J(A, B) is, the closer A and B are. When J(A, B) = 1, A is exactly the same as B, otherwise there is some difference between them. We measure similarity between two records based on their types. Therefore, our criteria find x_ref that is most similar to but not exactly the same with x.

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