imclab / babi-tasks

Task generation for testing text understanding and reasoning

bAbI tasks

This repository contains code to generate the bAbI tasks as described in the paper Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks. Please cite the paper if you use this code in your work.

Contents

• Installation
• Usage
• Tasks
  • Task flags
• Code Overview
  • World
  • Entity
  • Actions
  • Knowledge
  • Clause
  • Question
  • Natural language generation
• References

Installation

This project requires Torch to be installed. The easiest way to install Torch is by following the installation instructions at `torch/distro`__. To use the library, install it with LuaRocks by running the following command from the root directory.

luarocks make babitasks-scm-1.rockspec

Usage

To generate a task, run the command

babi-tasks <task-id>

where <task-id> is either a class name (like PathFinding) or the task number (e.g. 19). One can quickly generate 200 "stories" of each task with the following (NOTE: this is not equivalent to having 200 questions per task as there can be multiple questions per story, the original bAbI task data at http://fb.ai/babi specifically uses 1000 questions as training):

for i in `seq 1 20`; do babi-tasks $i 200 > task_$i.txt; done

Tasks

The tasks in babi/tasks correspond to those from the original dataset as follows:

#	Task	Class name
1	Basic factoid QA with single supporting fact	WhereIsActor
2	Factoid QA with two supporting facts	WhereIsObject
3	Factoid QA with three supporting facts	WhereWasObject
4	Two argument relations: subject vs. object	IsDir
5	Three argument relations	WhoWhatGave
6	Yes/No questions	IsActorThere
7	Counting	Counting
8	Lists/Sets	Listing
9	Simple Negation	Negation
10	Indefinite Knowledge	Indefinite
11	Basic coreference	BasicCoreference
12	Conjunction	Conjunction
13	Compound coreference	CompoundCoreference
14	Time manipulation	Time
15	Basic deduction	Deduction
16	Basic induction	Induction
17	Positional reasoning	PositionalReasoning
18	Reasoning about size	Size
19	Path finding	PathFinding
20	Reasoning about agent's motivation	Motivations

Note: This code is a rewrite of the original code that was used to generate the publicly available dataset at `fb.ai/babi`__. As such, it is not possible to produce exactly the same dataset. However, we have verified that numbers obtained are very similar.

Task flags

Some tasks accept configuration flags that will change their output.

In both the PathFinding and Size the number of inference steps required to answer the question can be changed. You can also control the number of "decoys" (locations that are not part of the path).

babi-tasks PathFinding --path-length 3 --decoys 1
babi-tasks Size --steps 3

Currently the path length plus the number of decoys has to be 5 or less. Similarly, the number of size comparisons cannot be more than 5.

For tasks involving people moving around, the use of coreferences and conjunctions can be controlled with the flags --coreference and --conjunction. These flags take a number between 0 and 1 as an argument, determining the fraction of the time coreferences and conjunctions are used respectively.

babi-tasks WhereIsActor --coreference 1.0

Tasks can also be rendered in a more symbolic manner. Use the flag --symbolic true to enable this.:

1 H teleport N
2 H teleport F
3 eval H is_in  F       2

Code Overview

Tasks are generated through simulation: We have a world containing entities, and actions that can add new entities to the world, or modify entities' states. Simulations then just take the form of sampling actions that are valid.

We often want to ask questions that require some sort of logical inference. Some types of inference can be re-used in multiple tasks, for example the deduction that a person and the object they are holding are in the same place is used several times. For this reason, some of the reasoning has been factored out: We keep track of what the reader of a story knows about the world, and each time a new line is read, we update this knowledge.

What follows is a brief overview of the classes and concepts used, which should help guide the understanding of the code.

World

A world is a collection of entities. Worlds can be loaded from text files such as those found in babi/tasks/worlds using the world:load(filename) command.

Entity

All concepts and objects in the simulations are entities. They are effectively Lua tables that describe the entity's properties.

Actions

Actions modify the state of the world. Each action is performed by an entity, even actions like setting the location or size of another entity (these are usually performed by the entity "god").

An action's is_valid method will test whether an action can be performed e.g. John cannot move to the kitchen if he is already there. The perform method assumes that the action is valid, and modifies the world accordingly i.e. it will change the location of John.

Lastly, actions can update the reader's knowledge of the world. For example, if we know that John is in the kitchen, the action "John grabs the milk" informs the reader that the milk is in the kitchen as well.

Knowledge

The Knowledge class keeps track of what a reader currently knows about the world. When actions are performed, the Action.update_knowledge method can update this knowledge accordingly. For example, when Knowledge contains the information that John is in the kitchen, the action of dropping the milk will result in the knowledge being updated to say that the milk is in the kitchen, and that it isn't being held by anyone.

The Knowledge class takes into account some basic logical rules. For example, some properties are "exclusive" in the sense that only one value can be true (John cannot be in the kitchen and the garden at the same time, but he can be not in the kitchen and not in the garden at the same time). Reversely, this means that if John is in the garden, the reader knows that he is not in the kitchen.

We keep track of which actions gave us which pieces of information about the world. This way, we can provide the user with the supporting facts when asking questions.

Clause

Facts about the world are expressed as clauses of the form (truth value, actor, action, arguments). For example (true, john, teleport, kitchen) means that John moved to the kitchen, while (false, john, drop, milk) means that John did not drop the milk. Note that because all information must be conveyed as actions, the sentence "John is in the garden" is represented as (true, god, set_property, is_in, garden).

Question

A question is represented as a tuple of the form (question type, clause, support).

1 John is in the garden.

2 Where is John? garden 1

This story is represented as a clause, clause = (true, god, set_property, john, is_in, garden), followed by a question, question = (evaluate, clause, {1}). A question like "Is john in the garden?" would instead be represented as question = (yes_no, clause, {1}).

Natural language generation

After the simulation is complete, a story (task) is nothing more but a list of clauses and questions. We turn this into text using the stringify function. This function performs a simple process: It repeatedly tries to find templates that can turn the next clause(s) or question(s) into text. It randomly samples a template from the matching ones, and goes on to the next clause that needs to be converted.

Templates can be selected further based on configuration (each task has a default configuration, but they can be passed through the command line as well). This enables turning on things like coreferences, conjunctions, etc.

References

• Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov. "`Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks`__", arXiv:1502.05698 [cs.AI].
• Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus, "`End-To-End Memory Networks`__", arXiv:1503.08895 [cs.NE].