Alsaidi, Safa

Couceiro, Miguel

Decker, Amandine

Lay, Puthineath

Marquer, Esteban

Murena, Pierre-Alexandre

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HAL Id: hal-03328841https://hal.inria.fr/hal-03328841Submitted on 30 Aug 2021HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Distributed under a Creative Commons Attribution| 4.0 International LicenseA Neural Approach for Detecting MorphologicalAnalogiesSafa Alsaidi, Amandine Decker, Puthineath Lay, Esteban Marquer,Pierre-Alexandre Murena, Miguel CouceiroTo cite this version:Safa Alsaidi, Amandine Decker, Puthineath Lay, Esteban Marquer, Pierre-Alexandre Murena, etal.. A Neural Approach for Detecting Morphological Analogies. IEEE DSAA 2021 - The 8th IEEEInternational Conference on Data Science and Advanced Analytics, Oct 2021, Porto / Online, Portugal., IEEE DSAA 2021. ￿hal-03328841￿A Neural Approach for Detecting Morphological AnalogiesSafa Alsaidi*, Amandine Decker*, Puthineath Lay*, Esteban Marquer*, Pierre-Alexandre Murena**, Miguel Couceiro**Université de Lorraine, CNRS, LORIA, F-54000, Nancy, France; **HIIT, Aalto University, Helsinki, FinlandProportional AnalogyNotation:I A : B :: C : D (read “A is to B as C is to D”)3 Core properties:1 A : B :: C : D −→ C : D :: A : B (symmetry)2 A : B :: C : D −→ A : C :: B : D (central permutation)3 A : B :: A : B (reflexively)Morphology:I “read is to readable as count is to countable”I aalto : aalloksi :: spirituaali : spirituaaliksi (Finnish)Training and Evaluation ProcedureAnalogies obtained by aligning morphologicaltransformations:I dog, to NUM=PLURAL, dogsI cat, to NUM=PLURAL, catsI resulting analogy: dog : dogs :: cat : catsFor each analogy A : B :: C : D extracted from thedata (use max 50000 of the available analogies):1 embed A, B, C, and D2 permute embeddings using properties ofproportional analogy:Train. & eval.: 8 valid analogiesTraining: 3 invalid analogiesEvaluation: 3× 8 invalid analogies and theirequivalent forms3 aggregate over all permutations:Training: loss (Binary Cross-Entropy)Evaluation: accuracyA : B :: C : D (base form) C : D :: A : BA : C :: B : D B : A :: D : C D : B :: C : AD : C :: B : A C : A :: D : B B : D :: A : CTable: 8 equivalent forms per analogyB : A :: C : D C : B :: A : D A : A :: C : DTable: 3 invalid analogies per analogyProposed Approach: Morphological Embedding + CNN ClassifierClassification ResultsNumber of analogiesCNN (ours) Best Baseline in the dataLanguage Valid Invalid Valid Invalid Training TestSigmorphon2016 task 1 (inflexion) [2]Arabic 99.89 97.52 34.21 (Alea@10) 97.79 (Kolmo@1) 373240 555312Finnish 99.44 82.62 25.60 (Lepage) 98.78* (Alea@1) 1342639 4691453Georgian 99.83 91.71 93.20 (Kolmo@10) 95.21 (Alea@1) 3553763 8368323German 99.48 89.01 86.90 (Alea@10) 97.19 (Alea@1) 994740 1480256Hungarian 99.99 98.81 36.80 (Kolmo@10) 98.40 (Kolmo@1) 3280891 66195Maltese 99.96 77.83 78.05 (Alea@10) 69.29 (Kolmo@1) 104883 3707Navajo 99.53 90.82 21.45 (Kolmo@10) 94.93 (Kolmo@1) 502637 4843Russian 97.95 79.85 42.37 (Alea@10) 93.88 (Lepage) 1965533 6421514Spanish 99.94 78.33 85.90 (Alea@10) 86.62 (Lepage) 1425838 4794504Turkish 99.48 92.63 44.76 (Alea@10) 91.40 (Kolmo@1) 606873 11360Japanese Bigger Analogy Test Set [4]Japanese 99.99 98.65 19.20 (Kolmo@10) 98.13 (Lepage) 18487 7923123 no significantdifference123 best result,significant differencebetween baselinesand ours* obtained on 4000analogies (too slowon 50000)TransferabilityPartial transfer (emb. of target clf. of source) Full transfer (emb. & clf. of source)Valid analogies Invalid analogies Valid analogies Invalid analogiesAlphabet gapBaselines1 Classifier:I Lepage Classifier [3]I matrix based approach to align characters betweenwords and find common sub-words2 Solvers:I find missing x making A : B :: C : x valid; ifexpected D in top 1 or 10 solutions, valid analogyI Kolmogorov Complexity [6]:I minimize complexity of f such that B = f (A) and f (C) iscomputable; x = f (C)I Alea [5]:I random permutations of the characters of B not in A andthose of C (Monte-Carlo method)PerspectivesË explore transferability [1]Ë balancing dataË regression (analogy solving)I qualitative analysis of embedding modelI other domains (images, text, explanations, etc.)Bibliography[1] Safa Alsaidi et al. “On the Transferability of Neural Modelsof Morphological Analogies”. In: AIMLAI, ECML PKDD2021. 2021.[2] Ryan Cotterell et al. “The SIGMORPHON 2016 SharedTask—Morphological Reinflection”. In: SIGMORPHON,2016. ACL, Aug. 2016.[3] Rashel Fam and Yves Lepage. “Tools for The Productionof Analogical Grids and a Resource of N-gram AnalogicalGrids in 11 Languages”. In: 11th LREC. ELRA, 2018.[4] Marzena Karpinska et al. “Subcharacter Information inJapanese embeddings: when is it worth it?” In: Workshopon the Relevance of Linguistic Structure in NeuralArchitectures for NLP. ACL, 2018.[5] Philippe Langlais et al. “Improvements in AnalogicalLearning: Application to Translating Multi-Terms of theMedical Domain”. In: 12th EACL. ACL, 2009.[6] Pierre-Alexandre Murena et al. “Solving Analogies onWords based on Minimal Complexity Transformation”. In:29th IJCAI. 2020.S. Alsaidi, A. Decker, P. Lay, E. Marquer, P.-A. Murena, M. Couceiro

A Neural Approach for Detecting Morphological Analogies

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A Neural Approach for Detecting Morphological Analogies

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