{"title":"Multi-Team: A Multi-attention, Multi-decoder Approach to Morphological Analysis.","authors":"A. Ustun, R. Goot, G. Bouma, Gertjan van Noord","doi":"10.18653/v1/W19-4206","DOIUrl":"https://doi.org/10.18653/v1/W19-4206","url":null,"abstract":"This paper describes our submission to SIGMORPHON 2019 Task 2: Morphological analysis and lemmatization in context. Our model is a multi-task sequence to sequence neural network, which jointly learns morphological tagging and lemmatization. On the encoding side, we exploit character-level as well as contextual information. We introduce a multi-attention decoder to selectively focus on different parts of character and word sequences. To further improve the model, we train on multiple datasets simultaneously and use external embeddings for initialization. Our final model reaches an average morphological tagging F1 score of 94.54 and a lemma accuracy of 93.91 on the test data, ranking respectively 3rd and 6th out of 13 teams in the SIGMORPHON 2019 shared task.","PeriodicalId":191536,"journal":{"name":"Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124937138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Inverting and Modeling Morphological Inflection","authors":"Yohei Oseki, Y. Sudo, Hiromu Sakai, A. Marantz","doi":"10.18653/v1/W19-4220","DOIUrl":"https://doi.org/10.18653/v1/W19-4220","url":null,"abstract":"Previous “wug” tests (Berko, 1958) on Japanese verbal inflection have demonstrated that Japanese speakers, both adults and children, cannot inflect novel present tense forms to “correct” past tense forms predicted by rules of existent verbs (de Chene, 1982; Vance, 1987, 1991; Klafehn, 2003, 2013), indicating that Japanese verbs are merely stored in the mental lexicon. However, the implicit assumption that present tense forms are bases for verbal inflection should not be blindly extended to morphologically rich languages like Japanese in which both present and past tense forms are morphologically complex without inherent direction (Albright, 2002). Interestingly, there are also independent observations in the acquisition literature to suggest that past tense forms may be bases for verbal inflection in Japanese (Klafehn, 2003; Murasugi et al., 2010; Hirose, 2017; Tatsumi et al., 2018). In this paper, we computationally simulate two directions of verbal inflection in Japanese, Present → Past and Past → Present, with the rule-based computational model called Minimal Generalization Learner (MGL; Albright and Hayes, 2003) and experimentally evaluate the model with the bidirectional “wug” test where humans inflect novel verbs in two opposite directions. We conclude that Japanese verbs can be computed online via some generalizations and those generalizations do depend on the direction of morphological inflection.","PeriodicalId":191536,"journal":{"name":"Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115692639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brad Aiken, Jared Kelly, Alexis Palmer, S. Polat, Taraka Rama, Rodney D. Nielsen
{"title":"Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few","authors":"Brad Aiken, Jared Kelly, Alexis Palmer, S. Polat, Taraka Rama, Rodney D. Nielsen","doi":"10.18653/v1/W19-4211","DOIUrl":"https://doi.org/10.18653/v1/W19-4211","url":null,"abstract":"This paper presents the UNT HiLT+Ling system for the Sigmorphon 2019 shared Task 2: Morphological Analysis and Lemmatization in Context. Our core approach focuses on the morphological tagging task; part-of-speech tagging and lemmatization are treated as secondary tasks. Given the highly multilingual nature of the task, we propose an approach which makes minimal use of the supplied training data, in order to be extensible to languages without labeled training data for the morphological inflection task. Specifically, we use a parallel Bible corpus to align contextual embeddings at the verse level. The aligned verses are used to build cross-language translation matrices, which in turn are used to map between embedding spaces for the various languages. Finally, we use sets of inflected forms, primarily from a high-resource language, to induce vector representations for individual UniMorph tags. Morphological analysis is performed by matching vector representations to embeddings for individual tokens. While our system results are dramatically below the average system submitted for the shared task evaluation campaign, our method is (we suspect) unique in its minimal reliance on labeled training data.","PeriodicalId":191536,"journal":{"name":"Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114205457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Augmenting a German Morphological Database by Data-Intense Methods","authors":"Petra C. Steiner","doi":"10.18653/v1/W19-4221","DOIUrl":"https://doi.org/10.18653/v1/W19-4221","url":null,"abstract":"This paper deals with the automatic enhancement of a new German morphological database. While there are some databases for flat word segmentation, this is the first available resource which can be directly used for deep parsing of German words. We combine the entries of this morphological database with the morphological tools SMOR and Moremorph and a context-based evaluation method which builds on a large Wikipedia corpus. We describe the state of the art and the essential characteristics of the database and the context method. The approach is tested on an inflight magazine of Lufthansa. We derive over 5,000 new instances of complex words. The coverage for the lemma types reaches up to over 99 percent. The precision of new found complex splits and monomorphemes is between 0.93 and 0.99.","PeriodicalId":191536,"journal":{"name":"Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121582647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexander Erdmann, Salam Khalifa, Mai Oudah, Nizar Habash, Houda Bouamor
{"title":"A Little Linguistics Goes a Long Way: Unsupervised Segmentation with Limited Language Specific Guidance","authors":"Alexander Erdmann, Salam Khalifa, Mai Oudah, Nizar Habash, Houda Bouamor","doi":"10.18653/v1/W19-4214","DOIUrl":"https://doi.org/10.18653/v1/W19-4214","url":null,"abstract":"We present de-lexical segmentation, a linguistically motivated alternative to greedy or other unsupervised methods, requiring only minimal language specific input. Our technique involves creating a small grammar of closed-class affixes which can be written in a few hours. The grammar over generates analyses for word forms attested in a raw corpus which are disambiguated based on features of the linguistic base proposed for each form. Extending the grammar to cover orthographic, morpho-syntactic or lexical variation is simple, making it an ideal solution for challenging corpora with noisy, dialect-inconsistent, or otherwise non-standard content. In two evaluations, we consistently outperform competitive unsupervised baselines and approach the performance of state-of-the-art supervised models trained on large amounts of data, providing evidence for the value of linguistic input during preprocessing.","PeriodicalId":191536,"journal":{"name":"Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133517651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"AX Semantics’ Submission to the SIGMORPHON 2019 Shared Task","authors":"Andreas Madsack, Robert Weißgraeber","doi":"10.18653/v1/W19-4201","DOIUrl":"https://doi.org/10.18653/v1/W19-4201","url":null,"abstract":"This paper describes the AX Semantics’ submission to the SIGMORPHON 2019 shared task on morphological reinflection. We implemented two systems, both tackling the task for all languages in one codebase, without any underlying language specific features. The first one is an encoder-decoder model using AllenNLP; the second system uses the same model modified by a custom trainer that trains only with the target language resources after a specific threshold. We especially focused on building an implementation using AllenNLP with out-of-the-box methods to facilitate easy operation and reuse.","PeriodicalId":191536,"journal":{"name":"Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117090717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"What do phone embeddings learn about Phonology?","authors":"Sudheer Kolachina, L. Magyar","doi":"10.18653/v1/W19-4219","DOIUrl":"https://doi.org/10.18653/v1/W19-4219","url":null,"abstract":"Recent work has looked at evaluation of phone embeddings using sound analogies and correlations between distinctive feature space and embedding space. It has not been clear what aspects of natural language phonology are learnt by neural network inspired distributed representational models such as word2vec. To study the kinds of phonological relationships learnt by phone embeddings, we present artificial phonology experiments that show that phone embeddings learn paradigmatic relationships such as phonemic and allophonic distribution quite well. They are also able to capture co-occurrence restrictions among vowels such as those observed in languages with vowel harmony. However, they are unable to learn co-occurrence restrictions among the class of consonants.","PeriodicalId":191536,"journal":{"name":"Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124217851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Weakly deterministic transformations are subregular","authors":"Andrew Lamont, C. O’Hara, Caitlin Smith","doi":"10.18653/v1/W19-4223","DOIUrl":"https://doi.org/10.18653/v1/W19-4223","url":null,"abstract":"Whether phonological transformations in general are subregular is an open question. This is the case for most transformations, which have been shown to be subsequential, but it is not known whether weakly deterministic mappings form a proper subset of the regular functions. This paper demonstrates that there are regular functions that are not weakly deterministic, and, because all attested processes are weakly deterministic, supports the subregular hypothesis.","PeriodicalId":191536,"journal":{"name":"Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123819571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Unbounded Stress in Subregular Phonology","authors":"Sophie Hao, Samuel A. Andersson","doi":"10.18653/v1/W19-4216","DOIUrl":"https://doi.org/10.18653/v1/W19-4216","url":null,"abstract":"This paper situates culminative unbounded stress systems within the subregular hierarchy for functions. While Baek (2018) has argued that such systems can be uniformly understood as input tier-based strictly local constraints, we show here that default-to-opposite-side and default-to-same-side stress systems belong to distinct subregular classes when they are viewed as functions that assign primary stress to underlying forms. While the former system can be captured by input tier-based input strictly local functions, a subsequential function class that we define here, the latter system is not subsequential, though it is weakly deterministic according to McCollum et al.’s (2018) non-interaction criterion. Our results motivate the extension of recently proposed subregular language classes to subregular functions and argue in favor of McCollum et al’s definition of weak determinism over that of Heinz and Lai (2013).","PeriodicalId":191536,"journal":{"name":"Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128166596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cross-lingual morphological inflection with explicit alignment","authors":"Çagri Çöltekin","doi":"10.18653/v1/W19-4209","DOIUrl":"https://doi.org/10.18653/v1/W19-4209","url":null,"abstract":"This paper describes two related systems for cross-lingual morphological inflection for SIGMORPHON 2019 Shared Task participation. Both sets of results submitted to the shared task for evaluation are obtained using a simple approach of predicting transducer actions based on initial alignments on the training set, where cross-lingual transfer is limited to only using the high-resource language data as additional training set. The performance of the system does not reach the performance of the top two systems in the competition. However, we show that results can be improved with further tuning. We also present further analyses demonstrating that the cross-lingual gain is rather modest.","PeriodicalId":191536,"journal":{"name":"Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134034109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}