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        <title>Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks</title>
        <author>Brenden M. Lake</author>
        <author>Marco Baroni</author>
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          <title>Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks</title>
          <date>2018</date>
          <publisher>ICML / arXiv</publisher>
          
          <idno type="arXiv">1711.00350</idno>
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        <label>systematic compositional generalization in sequence-to-sequence networks</label>
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        <term>simulation</term>
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        <term>A STIPULATED EQUIVALENCE CUTS THE TEST SPACE: commands are declared distributionally identical - defined as occurring in exactly the same composed commands - and that declaration is what makes testing the rest redundant. The sameness asserted is a property of the training distribution, not of the actions</term>
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        <term>THE DIFFERENCE BETWEEN TWO ITEMS IS EXPLAINED BY EXPOSURE RATHER THAN CAPACITY, and hedged twice while doing it: one primitive&#x27;s action is seen many times inside other directed actions, which APPARENTLY suffices, PROBABLY because the model gets direct evidence of how it is used in context - while the other action occurs only with its own primitive command and does not generalise from that minimal context</term>
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        <term>A NEAR-PERFECT SUCCESS IS DISQUALIFIED ON THE SHAPE OF ITS ERRORS RATHER THAN THEIR NUMBER: all 45 errors are conjunctions containing one of just two components (22 and 23 cases), both of which the model handles correctly on their own - and the authors say it is HARD TO CONCEIVE OF someone who understood the longer command but not the shorter one inside it. The error DISTRIBUTION, not the error rate, is what is read as measuring the capacity</term>
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        <term>A RIVAL EXPLANATION OF THE FAILURE IS ELIMINATED BY A SEPARATE MEASUREMENT: the network preferred its own output to the target by log-likelihood, so the errors are not search failures in the decoder - the model is not failing to FIND the right answer, it does not prefer it</term>
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        <term>THE SUFFICIENCY OF THE TRAINING DATA IS ARGUED SEPARATELY FROM THE RESULT AND MARKED AS IMPORTANT: the authors state that the training data of both experiments provide enough evidence to learn the composition rules that would afford the correct generalisations, and then spell out, per experiment, what was demonstrated during training</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <term>THE DEFLATIONARY READING OF THE RESULT IS STATED BY THE AUTHORS THEMSELVES AND THEN ANSWERED: the finding can be seen as a trivial restatement of the principle that train and test should come from the same distribution - and the reply is that human learners DO generalise to data governed by rules they have learned, so the triviality does not cover the case</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <term>AN ALTERNATIVE EXPLANATION OF A NULL IS ELIMINATED BY GRADED DOSING RATHER THAN ARGUMENT: composed examples of the new primitive are stepped 8 -&gt; 16 -&gt; 32 and accuracy climbs 38.3 -&gt; 77.8 -&gt; 88.4, appearing GRADUALLY instead of switching on, which rules out the reading that the network had simply lacked evidence</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <term>THE CAPACITY IS OPERATIONALISED AS A BEHAVIOURAL SIGNATURE, NOT AS AN ACCURACY: what is expected of a systematically compositional device is ALL-OR-NOTHING rule-based behaviour, so a network that generalises powerfully but needs more positive examples for it to emerge fails the definition WHILE SUCCEEDING AT THE TASK</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <term>THE AUTHORS NAME THEIR OWN TEST&#x27;S POSSIBLE UNFAIRNESS BEFORE ANYONE ELSE CAN: they say they tested systematicity in its purest form, then concede the setup is arguably too opaque for a computational model which could lack evidence that the two actions are the same sort of thing - and the concession is what motivates the follow-up experiment</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <term>THE CONTROL ITEM IS MATCHED ON THE TEST AND DELIBERATELY UNMATCHED ON THE TRAINING: a real adjective seen in 80 different constructions is tested on exactly the same eight constructions as the invented one and scores 8 of 8 against 1 of 8 - so the comparison isolates breadth of training context while holding the test fixed</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <term>THE POSITIVE HALF OF A NEGATIVE RESULT IS INSISTED ON RATHER THAN CONCEDED: the authors explicitly stress that the high zero-shot accuracy is an important positive result in controlled experiments, and add that understanding the mechanisms behind it is future work - refusing to let the headline failure absorb the finding that sits beside it</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>the SCAN benchmark grammar lacks recursion and generates only a finite command set</label>
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        <label>The machine-translation demonstration is small-scale and its result is described by the authors as preliminary.</label>
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        <label>The authors concede the Experiment 3 setup (a primitive seen only in isolation) may be unfairly opaque for a computational model that has no evidence the new action belongs to the same class as the others.</label>
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        <label>The benchmark&#x27;s name is an acronym whose first letter is its own admission of simplification.</label>
        <note>what this limits: any result reported as SCAN accuracy: the authors state the data set is called SCAN because it is Quotation withheld</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>The criterion the models are measured against comes from an argument the authors call controversial.</label>
        <note>what this limits: the claim that these networks lack systematicity: the standard comes from a set of papers the authors describe as influential and controversial, in which neural networks are argued to be associative devices that cannot capture systematic compositionality</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>Failure is defined by what an idealised agent would find trivial, not by a measured human baseline.</label>
        <note>what this limits: the description of these failures as catastrophic: the differences are said to be of the sort that would be trivial for an agent equipped with an algebraic mind</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>the systematicity failure is reframed using machine learning&#x27;s own assumption that training and test data come from the same distribution</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>the network re-trained with varying numbers of DISTINCT examples while the number of training presentations was held constant at 100K</label>
        <note>eliminates: diversity being confounded with volume - more distinct examples otherwise means more gradient steps, and the two cannot be separated · outcome (stated by the tagger, no quotation): held</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <term>SCAN</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>CommAI Navigation tasks</label>
        <term>CommAI Navigation tasks</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>systematic compositionality</label>
        <term>systematic compositionality</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>SCAN - AN ACRONYM THAT ADMITS ITS OWN REDUCTION: the S is for Simplified, because the data set is Quotation withheld; the expansion carries the scope limitation and the four letters that get cited do not</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>systematic compositionality - A CONTESTED PHILOSOPHICAL CRITERION USED AS A BENCHMARK: it comes from papers the authors themselves call influential and controversial, in which neural networks are argued to be associative devices that cannot capture it, so a SCAN score inherits an argument it cannot display</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>a catastrophic failure - RELATIVE TO AN ALGEBRAIC MIND: the models&#x27; errors are called catastrophic because the differences involved would be trivial for an agent equipped with an algebraic mind, which is a construct rather than a measured comparison group</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>builds-on → sequence-to-sequence learning methods</label>
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>extends → three decades of prior tests of compositional generalization in neural networks in the systematicity debate</label>
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        <label>RNNs generalize zero-shot when training and test commands differ only slightly, using mix-and-match strategies, but fail spectacularly when generalization needs systematic compositional skills.</label>
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        <label>A proof-of-concept in neural machine translation suggests lack of systematicity partly explains data-hungriness.</label>
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>SCAN domain</label>
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