Papers by Aizhan Imankulova
Neural Combinatory Constituency Parsing (2021.findings-acl)
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| Challenge: | Existing approaches to constituency parsing are based on symbolic engineering, but they are simplified by their adaptive distributed representation. |
| Approach: | They propose two fast combinatory models for constituency parsing: binary and multibranching. |
| Outcome: | The proposed models achieve an F1 score of 92.54 on Penn Treebank, speeding at 1327.2 sents/sec. |
Gender Bias in Masked Language Models for Multiple Languages (2022.naacl-main)
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| Challenge: | Masked Language Models (MLMs) pre-trained by predicting masked tokens on large corpora have been used successfully in natural language processing tasks for a variety of languages. |
| Approach: | They propose to use English attribute word lists to evaluate bias in eight languages without manually annotating data. |
| Outcome: | The proposed model significantly correlates with the existing English datasets for gender bias. |
Towards a Standardized Dataset on Indonesian Named Entity Recognition (2020.aacl-srw)
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| Challenge: | Named entity recognition (NER) tasks in the Indonesian language are still lacking data for the majority of languages, including Indonesian. |
| Approach: | They re-annotated an open dataset with 2,000 sentences and compared the results with a bidirectional long short-term memory and conditional random field approach. |
| Outcome: | The proposed approach improved the prediction score and consistent organization tag for the Indonesian language. |
Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019 (D19-52)
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| Challenge: | Using parallel corpora of different language pairs as training data is effective for multilingual neural machine translation model in extremely low resource situations. |
| Approach: | They propose to use Japanese-English and English-Russian parallel corpora as training data for their system to improve JapaneseRussian news translation. |
| Outcome: | The proposed system improves translation quality for JapaneseRussian language pairs in low resource situations. |
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding (2021.naacl-main)
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Rob van der Goot, Ibrahim Sharaf, Aizhan Imankulova, Ahmet Üstün, Marija Stepanović, Alan Ramponi, Siti Oryza Khairunnisa, Mamoru Komachi, Barbara Plank
| Challenge: | Lack of publicly available evaluation data for low-resource languages limits progress in SLU . despite advances in neural modeling for slot and intent detection, datasets for SLU remain limited. |
| Approach: | They propose a joint learning approach with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. |
| Outcome: | The proposed model can learn English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. |
Cross-lingual Transfer Learning for Grammatical Error Correction (2020.coling-main)
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| Challenge: | Existing studies on English GEC have focused on improving it, but the resources required to train the models are not sufficient. |
| Approach: | They investigate cross-lingual transfer learning in grammatical error correction tasks . similarities between these languages is a key factor for successfully transferring grammatikal knowledge . |
| Outcome: | The proposed methods improve accuracy of grammatical error correction tasks in English and Russian, but lack the resources to train models in these languages. |