Sei Iwata


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Universal Dependencies Treebank for Tatar: Incorporating Intra-Word Code-Switching Information
Chihiro Taguchi | Sei Iwata | Taro Watanabe
Proceedings of the Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference

This paper introduces a new Universal Dependencies treebank for the Tatar language named NMCTT. A significant feature of the corpus is that it includes code-switching (CS) information at a morpheme level, given the fact that Tatar texts contain intra-word CS between Tatar and Russian. We first outline NMCTT with a focus on differences from other treebanks of Turkic languages. Then, to evaluate the merit of the CS annotation, this study concisely reports the results of a language identification task implemented with Conditional Random Fields that considers POS tag information, which is readily available in treebanks in the CoNLL-U format. Experimenting on NMCTT and the Turkish-German CS treebank (SAGT), we demonstrate that the proposed annotation scheme introduced in NMCTT can improve the performance of the subword-level language identification. This annotation scheme for CS is not only universally applicable to languages with CS, but also shows a possibility to employ morphosyntactic information for CS-related downstream tasks.


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Zero Pronouns Identification based on Span prediction
Sei Iwata | Taro Watanabe | Masaaki Nagata
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop

The presence of zero-pronoun (ZP) greatly affects the downstream tasks of NLP in pro-drop languages such as Japanese and Chinese. To tackle the problem, the previous works identified ZPs as sequence labeling on the word sequence or the linearlized tree nodes of the input. We propose a novel approach to ZP identification by casting it as a query-based argument span prediction task. Given a predicate as a query, our model predicts the omission with ZP. In the experiments, our model surpassed the sequence labeling baseline.