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Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

Jason Weston, Antoine Bordes, Sumit Chopra

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Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov

2015 · arXiv

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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.

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5 selected excerpts · 5 total mentions

mike_hearn
Neutral mention · rule
Are you sure? I wrote an essay at the end of 2016 about the state of AI research and at the time researchers were demolishing benchmarks like FAIR's bAbI which involved generating answers to questions. I wrote back then about story comprehension and programming robots by giving them stories (we'd now call these prompts). https://blog.plan99.net/the-science-of-westworld-ec624585e47 bAbI paper: https://arxiv.org/abs/1502.05698 Abstract: 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…
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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. So at least FAIR was thinking about making AI that you could ask questions of in natural language. Then they went and beat their own benchmark with the Memory Networks paper: https://arxiv.org/pdf/1410.3916 Fred went to the kitchen. Fred picked up the milk. Fred travelled to the office. Where is the milk ? A: office Where does milk come from ? A: milk come from cow What is a cow a type of ? A: cow be female of cattle Where are cattle found ? A: cattle farm become widespread in brazil What does milk taste like ? A: milk taste like milk What does milk go well with ? A: milk go with coffee Where was Fred before the office ? A: kitchen That was published in 2015. So we can see quite early ChatGPT like capabilities, even though they're quite primitive still.
nl
Neutral mention · rule
Facebook have one of the leading teams on doing Deep Learning question answering. Jason Weston et. al wrote a paper introducing a machine-generated textual dataset[1] similar to what you describe, eg: Daniel picked up the football. Daniel drops the football. Daniel got the milk. Daniel took the apple. How many objects is Daniel holding? There have been plenty other papers published by them and others tackling that and similar but harder problems eg[2] [1]http://arxiv.org/pdf/1502.05698v10.pdf [2] http://arxiv.org/abs/1511.02301
andrewtbham
Neutral mention · rule
Here is a recent Facebook research paper that addresses the problem I believe this paper is intended to solve. Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks http://arxiv.org/pdf/1502.05698v6.pdf This recent paper is an algorithm to solve simple question answering from the context of a conversation... like the ones in the older paper... for instance. John is in the playground. Bob is in the office. Where is John? A:playground I believe a traditional LSTM RNN would have likely cleared the information about John from from it's long short term memory vector. Whereas you would still have access to the information about John in a stack. fta: "Since the machine can essentially learn how to operate its memory, it can learn how to program itself by producing sequences of instructions."
nl
Neutral mention · rule
It is true that this is an artificial test, but it seems to me that doing well at this is a necessary prerequisite to do well at more complex scenarios. It's worth noting that these tests were introduced by Jason Weston[1] as "A Set of Prerequisite Toy Tasks"[2] Also, they didn't report the accuracy on any of the other 18 tasks. That makes me believe that they did pretty badly on those. Weston's work on Memory Networks has "solved" (or at least shown a good way forward) on 16 of those other tasks. These were the two that were furthest away from being solved (See [2], again). The remaining 2 look like they will fall to Memory Networks with some work. [1] http://www.thespermwhale.com/jaseweston/ [2] http://arxiv.org/abs/1502.05698
nl
Neutral mention · rule
Here's a chronological reading list of techniques Traditional NLP -> 2012, IBM Watson papers[1] Watson is probably the pinnacle of "traditional" style NLP (ie, Tokenization/Lemmatization/Part of Speech Tagging/Framing/Knowledge Engineering/etc) Word2Vec -> 2013, Google paper[2] Word2Vec kind of exploded everyone's mind for a while. Subgraph Embedding -> 2015, Facebook AI Group [3][4] [1] http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=617771... [2] http://arxiv.org/pdf/1301.3781.pdf [3] https://research.facebook.com/publications/1473550739586509/... [4] http://arxiv.org/abs/1502.05698

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