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1. Introducing Morphology in Universal Dependencies Japanese

   Chihiro Taguchi and David Chiang ( January 2023 , Proceedings of the Sixth Workshop on Universal Dependencies (UDW, GURT/SyntaxFest 2023))

   This paper discusses the need for including morphological features in Japanese Universal Dependencies (UD). In the current version (v2.11) of the Japanese UD treebanks, sentences are tokenized at the morpheme level, and almost no morphological feature annotation is used. However, Japanese is not an isolating language that lacks morphological inflection but is an agglutinative language. Given this situation, we introduce a tentative scheme for retokenization and morphological feature annotation for Japanese UD. Then, we measure and compare the morphological complexity of Japanese with other languages to demonstrate that the proposed tokenizations show similarities to synthetic languages reflecting the linguistic typology. 

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2. Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections

   He, Junxian ; Zhang, Zhisong ; Berg-Kirkpatrick, Taylor ; Neubig, Graham ( July 2019 , Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics)

   Cross-lingual transfer is an effective way to build syntactic analysis tools in low-resource languages. However, transfer is difficult when transferring to typologically distant languages, especially when neither annotated target data nor parallel corpora are available. In this paper, we focus on methods for cross-lingual transfer to distant languages and propose to learn a generative model with a structured prior that utilizes labeled source data and unlabeled target data jointly. The parameters of source model and target model are softly shared through a regularized log likelihood objective. An invertible projection is employed to learn a new interlingual latent embedding space that compensates for imperfect crosslingual word embedding input. We evaluate our method on two syntactic tasks: part-ofspeech (POS) tagging and dependency parsing. On the Universal Dependency Treebanks, we use English as the only source corpus and transfer to a wide range of target languages. On the 10 languages in this dataset that are distant from English, our method yields an average of 5.2% absolute improvement on POS tagging and 8.3% absolute improvement on dependency parsing over a direct transfer method using state-of-the-art discriminative models. 

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3. Valency-Augmented Dependency Parsing

   https://doi.org/10.18653/v1/D18-1159  

   Shi, Tianze ; Lee, Lillian ( January 2018 , Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing)

   We present a complete, automated, and efficient approach for utilizing valency analysis in making dependency parsing decisions. It includes extraction of valency patterns, a probabilistic model for tagging these patterns, and a joint decoding process that explicitly considers the number and types of each token{'}s syntactic dependents. On 53 treebanks representing 41 languages in the Universal Dependencies data, we find that incorporating valency information yields higher precision and F1 scores on the core arguments (subjects and complements) and functional relations (e.g., auxiliaries) that we employ for valency analysis. Precision on core arguments improves from 80.87 to 85.43. We further show that our approach can be applied to an ostensibly different formalism and dataset, Tree Adjoining Grammar as extracted from the Penn Treebank; there, we outperform the previous state-of-the-art labeled attachment score by 0.7. Finally, we explore the potential of extending valency patterns beyond their traditional domain by confirming their helpfulness in improving PP attachment decisions. 

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4. Using Universal Dependencies in cross-linguistic complexity research

   https://doi.org/10.18653/v1/W18-6002  

   Berdicevskis, Aleksandrs ; Çöltekin, Çağrı ; Ehret, Katharina ; von Prince, Kilu ; Ross, Daniel ; Thompson, Bill ; Yan, Chunxiao ; Demberg, Vera ; Lupyan, Gary ; Rama, Taraka ; et al ( January 2018 , Proceedings of the Second Workshop on Universal Dependencies (UDW 2018))

   We evaluate corpus-based measures of linguistic complexity obtained using Universal Dependencies (UD) treebanks. We propose a method of estimating robustness of the complexity values obtained using a given measure and a given treebank. The results indicate that measures of syntactic complexity might be on average less robust than those of morphological complexity. We also estimate the validity of complexity measures by comparing the results for very similar languages and checking for unexpected differences. We show that some of those differences that arise can be diminished by using parallel treebanks and, more importantly from the practical point of view, by harmonizing the language-specific solutions in the UD annotation. 

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5. Jointly Parse and Fragment Ungrammatical Sentences

   Hashemi, Homa B ; Hwa, Rebecca ( February 2018 , Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence)

   This paper is about detecting incorrect arcs in a dependency parse for sentences that contain grammar mistakes. Pruning these arcs results in well-formed parse fragments that can still be useful for downstream applications. We propose two automatic methods that jointly parse the ungrammatical sentence and prune the incorrect arcs: a parser retrained on a parallel corpus of ungrammatical sentences with their corrections, and a sequence-to sequence method. Experimental results show that the proposed strategies are promising for detecting incorrect syntactic dependencies as well as incorrect semantic dependencies. 

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