MachineComprehension

Problem Statement

Example: Task with single supporting fact

Comprehension:

John is in the kitchen. Bob is in the gardeb.

Query:

Where is John?

Answer:

kitchen (one with max. probability)

OVERVIEW

• Neural Network model with external memory.
• Reads the memory with soft attention.
• It accesses memory multiple times; each step being called a hop.
• Uses back propagation to update the model.

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	Negatio
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

Requirements

• Python 3 and above

• Numpy, Flask, TensorFlow (only for web-based demo) can be installed via pip:

• bAbI dataset should be downloaded to data/tasks_1-20_v1-2:

$ wget -qO- http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz | tar xvz -C data

DOCKER IMAGE FOR WEB APP

docker pull tushargl016/cognitivefinalproject
docker run -p 80:8000 -d -ti tushargl016/cognitivefinalproject

References

• Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus, "End-To-End Memory Networks", arXiv:1503.08895 [cs.NE].

Link to Web: http://ec2-54-161-78-119.compute-1.amazonaws.com/