Lexica distinguishing all morphologically related forms of each lexeme are crucial to many language technologies , yet building them is expensive . We propose Frugal Paradigm Completion , an approach that predicts all related forms in a morphological paradigm from as few manually provided forms as possible . It induces typological information during training which it uses to determine the best sources at test time . We evaluate our language-agnostic approach on 7 diverse languages . Compared to popular alternative approaches , our Frugal Paradigm Completion approach reduces manual labor by 16 - 63 % and is the most robust to typological variation . From syntactic parsing ( Seeker and Kuhn , 2013 ) to text-to-speech ( Zen et al . , 2016 ; Wan et al . , 2019 ) , many linguistic technologies rely on accurate lexica decorated with morphological information . Yet , building such lexica requires much human effort ( Buckwalter , 2002 ; Tadić and Fulgosi , 2003 ; Forsberg et al . , 2006 ; Sagot , 2010 ; Eskander et al . , 2013 ) . We present a language-agnostic method for minimizing the manual labor required to add new paradigms to an existing lexicon . Formally , let each lexicon entry , or realization , be a triple ( P , C , f ) . P marks membership in some paradigm P of morphologically related words , C defines a cell in P as a bundle of morphosyntactic features , and f is the form realizing C in P. Hence , paradigm SING can be expressed ( in the UniMorph schema ( Kirov et al . , 2018 ) ) as a set of realizations : { ( SING , NFIN , sing ) , ( SING , 3.SG.PRES , sings ) , . . . } . For each paradigm to be added to the lexicon , e.g. , FLY , we aim to select as few sources as pos-sible to be manually realized , e.g. , { ( FLY , NFIN , fly ) , ( FLY , PST , flew ) } such that the forms realizing the remaining cells can be predicted , i.e. , flies , flying , flown . Here , sources are manually provided realizations . Targets are realizations whose forms must be predicted from sources . Our work differs from traditional paradigm completion ( Durrett and DeNero , 2013 ) in that sources are not given blindly , but the system must strategically select which sources it wants to be given at test time . Paradigm completion from one source is typically non-deterministic due to multiple inflection classes realizing different exponents in some cells , e.g. , suffixing + ed generates the past tense for WALK , but not for SING or FLY which are members of different classes . Hence , many works discuss paradigm completion in the context of ( implicit ) inflection class disambiguation ( Ackerman et al . , 2009 ; Montermini and Bonami , 2013 ; Beniamine et al . , 2018 ) . Finkel and Stump ( 2007 ) propose three approaches to select the fewest sources required to deterministically identify class . Yet , neural sequence models can often complete paradigms accurately from less sources without fully disambiguating inflection class ( Kann and Schütze , 2016 ; Aharoni and Goldberg , 2017 ; Wu and Cotterell , 2019 ) . See Elsner et al . ( 2019 ) for an overview of the application of neural sequence models to morphological theory . We propose Frugal Paradigm Completion ( FPC ) , inspired by work on inflection class disambiguation and neural sequence modeling . We train a source selection agent ( SSA ) to induce typological knowledge regarding the distribution of complexity in paradigms and use this to request informative source cells to be realized by an oracle . Sources are fed to a predictor to generate target forms . For each paradigm , SSA iteratively requests sources until the oracle confirms all cells have been realized correctly . We introduce a novel metric , auto-rate , to quantify the manual labour ( performed by the oracle ) needed to complete each paradigm . Using this metric , we demonstrate that FPC reduces labor by 63 % over predicting targets from lemmata , and 47 % over predicting them from the smallest set of sources that fully disambiguates inflection class . We propose a new typology for discussing the organization of complexity in paradigms which helps explain why strategies perform better or worse on certain languages while FPC , being sensitive to typological variation , performs robustly . After discussing related paradigm completion approaches in Section 2 , we describe FPC in Section 3 . Section 4 covers all data and experimental set up details . We discuss results in Section 5 and analyze FPC 's behavior in Section 6 . We presented Frugal Paradigm Completion , which reduces the manual labor required to expand a morphological lexicon by 16 - 63 % over competitive approaches across 7 languages . We demonstrated that typologically distinct morphological systems require unique treatment and benefit from our SSA , that learns its strategy from data . We found that inducing this strategy is not as challenging as previously suggested ( Finkel and Stump , 2007 ) . Thus , SSA might be replaced with a less costly architecture while our model might be improved by conditioning on semantics and jointly decoding from a variable number of sources .

Challenge: Lexica distinguishing all morphologically related forms is crucial to language technologies but building such a resource requires expensive human effort.
Approach: They propose a language-agnostic method which predicts all related forms in a morphological paradigm from a few manual annotations by inducing typological information during training.
Outcome: The proposed method reduced the manual labor for expanding a morphological lexicon by 16 - 63% over existing methods in 7 languages.