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Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov
TL;DR
The paper addresses how to measure progress toward systems that understand and reason over natural language when broad QA and dialogue evaluations are difficult to diagnose. It introduces a simulation-generated suite of skill-focused QA tasks and extends Memory Networks; the models solve some tasks but fail others, and the datasets remain unsolved under weak supervision.
Problem
Broad QA and dialogue evaluations make it difficult to identify which text-understanding and reasoning capabilities systems possess or lack.
Method
The paper builds a self-contained suite of synthetic QA tasks from a simulated world and evaluates Memory Networks with extensions across these skill-focused tests.
Results
Memory Networks outperform N-gram and LSTM baselines on many tasks but still fail several tasks, including some requiring yes/no and multi-fact reasoning.
Takeaways & Limitations
The tasks provide prerequisite tests for diagnosing reasoning mechanisms, and their feedback loop has influenced subsequent learning algorithms.
Takeaways & Limitations
The datasets are not yet solved, and weakly supervised settings with 1000 training examples or fewer lack a known general non-hand-engineered solution.
Abstract
from arXiv · showhide
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.
1 INTRODUCTION
The paper proposes synthetic question-answering tasks as a controlled framework for developing text-understanding and reasoning algorithms. A simulation generates grounded text and question-answer pairs, enabling skill-based diagnosis and iterative model improvement.
- Motivation: Synthetic tasks offer a controlled alternative for developing techniques when large real datasets favor simpler models.The paper frames synthetic data as a way to escape local minima in algorithm design.
- Framework: The framework uses question answering because responses are easier to evaluate than general dialogue, while supporting many capability-focused tasks.QA provides a common setup for testing different aspects of learning algorithms.
- Framework: A physical-world simulation generates grounded text and question-answer pairs as actors move, manipulate objects, and interact.The generated questions are categorized into skill sets that define the tasks.
- Research loop: The task analysis is intended to expose current-model weaknesses and motivate new algorithms, including adversarially designed tasks in an iterative feedback loop.The paper also benchmarks standard methods and proposes Memory Network improvements as an example of this loop.
2 RELATED WORK
Existing broad QA benchmarks are difficult to interpret because solving their questions combines many capabilities and often relies on handcrafted features, large corpora, or domain knowledge. The paper therefore introduces simpler, diverse, self-contained tasks whose individual outcomes provide clearer capability feedback.
- Benchmark limitations: Broad QA benchmarks are difficult to interpret because each question may require coreference, deduction, common sense, and other skills simultaneously.The paper says no system had fully solved the proposed tasks, complicating diagnosis of capabilities and limitations.
- Benchmark limitations: Top-performing systems often rely on handcrafted patterns, features, or very large corpora, making their language understanding difficult to establish.A more structured alternative remains domain-specific and dependent on substantial prior knowledge.
- Proposed alternative: The proposed tasks are simpler so success or failure on an individual task can provide unequivocal feedback about a system’s capabilities.This design follows observations about the interpretive difficulty of broader initiatives.
- Proposed alternative: Unlike the Winograd Challenge, the tasks are self-contained, diverse, and supplied with both training and evaluation data.The comparison distinguishes the paper’s task setup from benchmarks that provide only evaluation data.
- Scope: The authors postulate that performing well on all tasks is a prerequisite for systems aiming at full text understanding and reasoning.
3 THE TASKS
The bAbI task suite uses software-testing principles to isolate language-understanding and reasoning skills in synthetic QA problems. It provides generated training and test data with simple exact-answer evaluation, spanning fact chaining, relations, temporal and spatial reasoning, induction, and motivations.
- Principles: The suite treats tasks like software tests, aiming for independent leaf cases that isolate one behavior before combining skills in later tests.The tasks and their generation code are publicly available.
- Evaluation: Each task supplies training and test data, relevant supporting statements, and answers evaluated as single words or word lists.Performance is measured simply as right or wrong.
- Data generation: A simulation of characters and objects moving and interacting generates scenarios with known labels grounded in the simulated world.Sample statements, questions, and true labels are presented for the tasks.
- Basic reasoning: Tasks 1–3 test single-fact retrieval and chaining across two or three supporting facts, while tasks 4–5 test argument roles and word-order sensitivity.Task 2 combines information about a person’s location and an object they picked up; task 4 defeats bag-of-words matching.
- Basic reasoning: Tasks 6–10 cover yes/no questions, counting, lists, negation, and indefinite knowledge such as possibilities rather than certainties.These tasks extend basic QA with Boolean, set-like, and uncertainty-related constructs.
- Evaluation principles: The suite is intended for one model evaluated across all tasks and then tested on additional real-world tasks, without per-task tuning.
4 SIMULATION
The tasks are generated inside a controlled text-adventure simulation whose coherent world supports grounded language and question-answer pairs. Its limited vocabulary and linguistic complexity make it a complement to, rather than a substitute for, real data.
- Generation: The simulation generates grounded text and question-answer pairs as actors interact with objects and locations.This provides a controlled environment for evaluating language understanding and reasoning tasks.
- World model: The simulation models entities, internal states, spatial relations, and actions in a coherent artificial world.Entities include locations, objects, and persons; states include location, containment, mental properties, and attributes such as size, color, and edibility.
- World model: Actors execute constrained actions such as moving, acquiring, placing, giving, dropping, inspecting, and changing entity states.Universal constraints prevent incoherent actions, such as acquiring an object already held or moving to an unconnected location.
- Text generation: A simple automated grammar creates lexical variety by replacing commands, objects, and actors with synonyms.For example, the command get can become picked up, got, grabbed, or took.
- Scope: The environment uses short sentences, a small vocabulary of 150 words, and typically four actors, six locations, and three objects per task.The authors state that these tasks should complement real data rather than replace it.
5 EXPERIMENTS
The experiments compare several weakly and strongly supervised QA models and test extensions to Memory Networks across 20 tasks. Standard Memory Networks outperform N-gram and LSTM baselines, while combining adaptive memory, N-grams, and nonlinearities improves nine tasks from failure to success but leaves important limitations.
- Experimental setup: The evaluation compares N-gram classifiers, LSTMs, Memory Networks, Memory Network extensions, and a structured SVM across 20 tasks.The methods span weak supervision from question-answer pairs, strong supervision using supporting facts, and external labeled resources.
- Baseline results: Standard Memory Networks generally outperform the N-gram and LSTM baselines but fail on several tasks, including unexpected failures on yes/no and indefinite-knowledge questions.The authors attribute the yes/no failure to the inability of a linear scoring function to model three-way interactions among the query, supporting fact, and answer.
- Memory Network extensions: Adaptive memory improves tasks requiring more than two supporting facts and gives smaller gains on tasks requiring multi-word outputs.The authors combine adaptive memory with the other proposed extensions in subsequent experiments.
- Memory Network extensions: N-gram modeling improves tasks where word order matters, whereas nonlinear matching performs better on average and helps yes/no and indefinite tasks.Nonlinear matching cannot model word order, so the two extensions address different weaknesses.
- Combined model: 9 tasks are upgraded from failure to success by combining adaptive memory, N-grams, and nonlinearities compared with the original Memory Network.The combined AM+NG+NL model improves over each complementary extension individually.
- Structured SVM: The structured SVM still fails at 9 tasks despite external resources and performs worse than AM+NG+NL on tasks requiring multiple supporting facts.Its non-greedy search helps on path finding, where search is important, but its English-specific resources may not transfer equally well to other languages.
- Unsolved tasks: Positional reasoning and path finding cannot be solved even with 10000 examples, suggesting that these tasks require general search in inference.The tested Memory Network and other approaches lack such a search algorithm.
- Joint training: Joint training on all tasks produces generally encouragingly similar performance while still failing several tasks and using stronger supervision than is typically realistic.The joint-training result indicates that the model can learn many aspects of text understanding and reasoning simultaneously.
6 DISCUSSION
The tasks form a flexible prerequisite test bed for language understanding and reasoning, exposing model capabilities while complementing rather than replacing real-world data. Experiments show Memory Networks solve some tasks, but current methods still fail others and often require unrealistic supervision.
- The task set targets prerequisite capabilities for full language understanding and reasoning, though solving it does not imply full reasoning.
- A flexible framework: Researchers can vary task complexity, language, ambiguity, memory placement, and story realism because the simulation framework is not definitive.
- Testing learning methods: Memory Networks succeed on some tasks but still fail several, despite useful extensions and stronger supervision than is typically realistic.
- Testing learning methods: The datasets remain unsolved: with weak supervision and 1000 training examples or fewer, no known general non-hand-engineered method solves them.
- The tasks should complement real data, and success on the 20 tasks should also be demonstrated on real-world data.
- Impact: The tasks have influenced subsequent algorithms, including weakly supervised end-to-end Memory Networks, Dynamic Memory Networks, and the Neural Reasoner.
A EXTENSIONS TO MEMORY NETWORKS
The Memory Network architecture maps inputs into memory, selects supporting facts, and decodes an answer. Its extensions address temporal information and response generation while retaining learned embedding-based scoring.
- Memory Networks use input, generalization, output, and response components operating over an indexed memory of story statements.
- The simplest generalization stores each incoming example in an empty memory slot, leaving existing memory unchanged.
- The output module retrieves k = 2 supporting memories by scoring the input and then conditioning the second selection on the first.
- The response module normally ranks dictionary words to produce a single-word answer, with sR providing the matching score.
- The scoring functions use embedding models that map text into feature space; the implementation uses bag-of-words features and separate representations for inputs and memories.
- Write-time features encode which sentence is older when memories are compared, supplying temporal information unavailable directly to the original model.
A.1 SHORTCOMINGS OF THE EXISTING MEMNNS
Existing Memory Networks have structural limitations: fixed two-hop retrieval, bag-of-words representations, and single-word response decoding restrict the tasks they can solve.
- Bag-of-words sentence representations are expected to fail on two-argument and three-argument relation tasks.
- Fixed k = 2 max operations cannot handle questions requiring more than two supporting facts, including tasks 3 and 7.
- Without an RNN response module, standard Memory Networks cannot output multiple answers required by list and path-finding tasks.
A.2.1 ADAPTIVE MEMORIES (AND RESPONSES)
The adaptive-memory extension selects a variable number of supporting facts and generates multiple-word responses by stopping when learned sentinel symbols are predicted.
- Supporting-fact selection becomes adaptive by scoring a special fact m∅ and repeatedly selecting facts until that sentinel is predicted.
- The sentinel m∅ has a learned embedding, while experiments impose a hard limit of 10 loops to prevent non-terminating computation.
- Multiple-word responses are generated iteratively by predicting each word conditioned on the input, selected memories, and previous words until w∅ is predicted.
A.2.2 NONLINEAR SENTENCE MODELING
The paper explores nonlinear sentence representations beyond bag-of-words, including position-sensitive multilinear maps and nonlinear embeddings. The multilinear approach performs comparably to the strongest N-gram combination while avoiding its rapidly growing dictionary.
- A.2.2 NONLINEAR SENTENCE MODELING: Three variants extend sentence modeling beyond a bag-of-words: bag-of-N-grams, multilinear maps, and nonlinear embeddings.The N-gram variant uses N = 1, 2, and 3; the other approaches use neural mappings.
- A.2.2 NONLINEAR SENTENCE MODELING: The multilinear map bins each word by sentence position and applies a position-specific n × n matrix before computing the matching score.The resulting mappings are summed and passed through a tanh nonlinearity.
- A.2.2 NONLINEAR SENTENCE MODELING: 93 mean performance: MemNN with adaptive memories plus multilinear matches MemNN with adaptive memories, N-grams, and nonlinear layers.The multilinear method may also be useful when N-grams make the dictionary too large.
- A.2.2 NONLINEAR SENTENCE MODELING: The nonlinear embedding applies a two-layer neural-network-style transformation to both the question and document representations.It uses nested tanh nonlinearities and a shared n × n matrix W.
B BASELINE USING EXTERNAL RESOURCES
The external-resource baseline combines structured SVMs with coreference resolution, semantic role labeling, and structured searches over supporting facts. It does not outperform Memory Networks overall, but its feature design and non-greedy search help on different task types.
- B BASELINE USING EXTERNAL RESOURCES: The baseline preprocesses stories with Stanford coreference resolution and SENNA semantic role labeling before structured SVM prediction.Coreference mentions are replaced with their entities’ first mentions, and SRL features are used in the model.
- B BASELINE USING EXTERNAL RESOURCES: The supporting-fact stage exhaustively searches for up to three facts using a linear scoring function, with pruning based on shared non-determiner words.The search can also select an empty fact when fewer than three supporting facts are needed.
- B BASELINE USING EXTERNAL RESOURCES: The structured SVM fails at 9 tasks and does not perform better than Memory Networks overall despite access to external resources.It performs well on tasks 6, 9, and 10, where hand-built feature conjunctions capture nonlinearities absent from the original Memory Networks.
- B BASELINE USING EXTERNAL RESOURCES: Non-greedy search helps path finding but performs worse on tasks requiring two or three supporting facts.Ranking over many possibilities apparently introduces more mistakes, while search is important for task 19.