Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Paper summary #### Introduction The [paper](http://arxiv.org/pdf/1502.05698v10) presents a framework and a set of synthetic toy tasks (classified into skill sets) for analyzing the performance of different machine learning algorithms. #### Tasks * **Single/Two/Three Supporting Facts**: Questions where a single(or multiple) supporting facts provide the answer. More is the number of supporting facts, tougher is the task. * **Two/Three Supporting Facts**: Requires differentiation between objects and subjects. * **Yes/No Questions**: True/False questions. * **Counting/List/Set Questions**: Requires ability to count or list objects having a certain property. * **Simple Negation and Indefinite Knowledge**: Tests the ability to handle negation constructs and model sentences that describe a possibility and not a certainty. * **Basic Coreference, Conjunctions, and Compound Coreference**: Requires ability to handle different levels of coreference. * **Time Reasoning**: Requires understanding the use of time expressions in sentences. * **Basic Deduction and Induction**: Tests basic deduction and induction via inheritance of properties. * **Position and Size Reasoning** * **Path Finding**: Find path between locations. * **Agent's Motivation**: Why an agent performs an action ie what is the state of the agent. #### Dataset * The dataset is available [here](https://research.facebook.com/research/-babi/) and the source code to generate the tasks is available [here](https://github.com/facebook/bAbI-tasks). * The different tasks are independent of each other. * For supervised training, the set of relevant statements is provided along with questions and answers. * The tasks are available in English, Hindi and shuffled English words. #### Data Simulation * Simulated world consists of entities of various types (locations, objects, persons etc) and of various actions that operate on these entities. * These entities have their internal state and follow certain rules as to how they interact with other entities. * Basic simulations are of the form: <actor> <action> <object> eg Bob go school. * To add variations, synonyms are used for entities and actions. #### Experiments ##### Methods * N-gram classifier baseline * LSTMs * Memory Networks (MemNNs) * Structured SVM incorporating externally labeled data ##### Extensions to Memory Networks * **Adaptive Memories** - learn the number of hops to be performed instead of using the fixed value of 2 hops. * **N-grams** - Use a bag of 3-grams instead of a bag-of-words. * **Nonlinearity** - Apply 2-layer neural network with *tanh* nonlinearity in the matching function. ##### Structured SVM * Uses coreference resolution and semantic role labeling (SRL) which are themselves trained on a large amount of data. * First train with strong supervision to find supporting statements and then use a similar SVM to find the response. ##### Results * Standard MemNN outperform N-gram and LSTM but still fail on a number of tasks. * MemNNs with Adaptive Memory improve the performance for multiple supporting facts task and basic induction task. * MemNNs with N-gram modeling improves results when word order matters. * MemNNs with Nonlinearity performs well on Yes/No tasks and indefinite knowledge tasks. * Structured SVM outperforms vanilla MemNNs but not as good as MemNNs with modifications. * Structured SVM performs very well on path finding task due to its non-greedy search approach.
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Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Jason Weston and Antoine Bordes and Sumit Chopra and Alexander M. Rush and Bart van Merriënboer and Armand Joulin and Tomas Mikolov
arXiv e-Print archive - 2015 via arXiv
Keywords: cs.AI, cs.CL, stat.ML

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