sempre/tables

Compositional Semantic Parsing on Semi-Structured Tables

This README file describes the tables mode which accompanies the paper:

Panupong Pasupat, Percy Liang.
Compositional Semantic Parsing on Semi-Structured Tables
Association for Computational Linguistics (ACL), 2015.

Further descriptions and experiments can be viewed on the CodaLab website:

https://www.codalab.org/worksheets/0xf26cd79d4d734287868923ad1067cf4c/

Running the code

1. Download the dependencies and the dataset:

    ./pull-dependencies core
    ./pull-dependencies corenlp
    ./pull-dependencies tables
    ./pull-dependencies tables-data
   

The dataset lives in lib/data/tables/

2. Compile the source:

    ant tables
   

This will produce JAR files in the libsempre directory as usual.

3. The following command train and test on 100 development examples:

    ./run @mode=tables @data=u-1 @feat=all @train=1 -maxex train,100 dev,100
   

The command should take less than an hour.

• To train on the complete development set, remove -maxex train,100 dev,100

• The command above uses u-1 (80:20 split of the development data). Other available sets include u-2, ..., u-5 (four other development splits) and test (actual train-test split).

Other usages

To launch the interactive shell, use:

./run @mode=tables -interactive

Apart from the usual shell commands, the additional command context can load the context graph for execution. For example, use:

(context (graph tables.TableKnowledgeGraph csv/204-csv/590.csv))

to load lib/data/tables/csv/204-csv/590.csv

The table can also be viewed in pretty-printed format by calling

./tables/view 204 590

or

./tables/view csv/204-csv/590.csv