Papers by A. Seza Doğruöz
Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+ (2026.eacl-srw)
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York Hay Ng, Aditya Khan, Xiang Lu, Matteo Salloum, Michael Zhou, Phuong Hanh Hoang, A. Seza Doğruöz, En-Shiun Annie Lee
| Challenge: | Existing linguistic knowledge bases such as URIEL+ lack a principled method for aggregating these signals into a single, comprehensive score. |
| Approach: | They propose a framework for type-matched language distances that unifies these signals into a robust, task-agnostic composite distance. |
| Outcome: | The proposed representations improve transfer performance when the distance type is relevant to the task, while yielding gains in most tasks. |
Are they lovers or friends? Evaluating LLMs’ Social Reasoning in English and Korean Dialogues (2026.acl-long)
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Eunsu Kim, Junyeong Park, Juhyun Oh, Kiwoong Park, Seyoung Song, A. Seza Doğruöz, Alice Oh, Najoung Kim
| Challenge: | Existing studies on LLMs' ability to infer social relationships have limited results for Korean and English. |
| Approach: | They propose a social reasoning task based on a 1.1k-dialogue dataset in English and Korean sourced from movie scripts to evaluate LLMs' ability to infer the social relationships between speakers. |
| Outcome: | The proposed task evaluates the ability of LLMs to infer the social relationships between speakers in 1.1k-dialogue datasets in English and Korean. |
Does Generative AI speak Nigerian-Pidgin?: Issues about Representativeness and Bias for Multilingualism in LLMs (2025.findings-naacl)
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| Challenge: | Nigeria is a multilingual country with 500+ languages. |
| Approach: | They propose to use a pidgin and a creole to analyze the pidgins of Nigeria . they also use machine translation to analyze their results . |
| Outcome: | The results show that the two pidgins do not represent each other and are hard to teach . the results show the pidgin varieties are underrepresented in Generative AI . |
Is Spoken Hungarian Low-resource?: A Quantitative Survey of Hungarian Speech Data Sets (2024.lrec-main)
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Peter Mihajlik, Katalin Mády, Anna Kohári, Fruzsina Sára Fruzsina, Gábor Kiss, Tekla Etelka Gráczi, A. Seza Doğruöz
| Challenge: | Existing data sets in Hungarian are limited in quality and quality . however, it is difficult to train a modern automatic speech recognition system with thousands of hours of transcribed speech. |
| Approach: | They propose to analyze available speech data sets in Hungarian in five categories . they estimate that the available data sets are 2800 hours across 7500 speakers . |
| Outcome: | The available data sets in spoken Hungarian are compared to other languages and are estimated to be 2800 hours in size . however, their distribution and alignment to real-life tasks are far from optimal indicating the need for larger-scale natural language speech data sets. |
A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies (2021.acl-long)
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| Challenge: | linguistic and social aspects of code-switching are not discussed in the literature in linguistics. |
| Approach: | They propose to examine linguistic and social aspects of code-switching across a wide range of languages in a survey of the literature in linguistics and language technologies. |
| Outcome: | The proposed framework aims to increase the clarity and depth of computational investigations of C-S and bridge the fields so that they might be mutually reinforcing. |
Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity (2024.findings-eacl)
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Eric Khiu, Hasti Toossi, Jinyu Liu, Jiaxu Li, David Anugraha, Juan Flores, Leandro Roman, A. Seza Doğruöz, En-Shiun Lee
| Challenge: | Existing approaches for predicting the performance of NLP models for low-resource languages (LRLs) focus on high-resourced languages, overlooking LRLs and domain shifts. |
| Approach: | They investigate the impact of domain similarity on predicting performance of machine translation models in low-resource languages. |
| Outcome: | The results show that domain similarity has the most important impact on predicting the performance of Machine Translation models. |
URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base (2025.coling-main)
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Aditya Armaan Khan, Mason Stephen Shipton, David Anugraha, Kaiyao Duan, Phuong H. Hoang, Eric Khiu, A. Seza Doğruöz, Annie Lee
| Challenge: | URIEL is limited in terms of linguistic inclusion and overall usability . URIel+ provides robust, customizable distance calculations to better suit the needs of users. |
| Approach: | They propose a new version of URIEL and a query tool that provides a standardized approach to representing languages as geographical, phylogenetic, and typological vectors. |
| Outcome: | URIEL+ expands the user experience with robust, customizable distance calculations to better suit the needs of users. |
Automatic Identification and Classification of Bragging in Social Media (2022.acl-long)
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| Challenge: | Bragging is a speech act employed to build a favorable self-image through positive statements about oneself. |
| Approach: | They propose to use tweets annotated for bragging to build a model that can predict bragging with macro F1 up to 72.42 and 35.95 for binary and multi-class bragging classification tasks respectively. |
| Outcome: | The proposed models predict bragging with macro F1 up to 72.42 and 35.95 in binary and multi-class classification tasks respectively. |
Who Is Bragging More Online? A Large Scale Analysis of Bragging in Social Media (2024.lrec-main)
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| Challenge: | Social media is a natural platform for users to use bragging to gain admiration, respect, attention and followers from their audiences. |
| Approach: | They employ computational sociolinguistics methods to conduct the first large scale study of bragging behavior on Twitter by focusing on its overall prevalence, temporal dynamics and impact of demographic factors. |
| Outcome: | The proposed study shows that the prevalence of bragging decreases over time within the same population of users and younger, more educated and popular users in the U.S. are more likely to brag. |
Representativeness as a Forgotten Lesson for Multilingual and Code-switched Data Collection and Preparation (2023.findings-emnlp)
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| Challenge: | Multilingualism is widespread around the world and code-switching (CSW) is a common practice among different language pairs/tuples across locations and regions. |
| Approach: | They propose to use existing CSW data sets to improve the representativeness of CSW datasets. |
| Outcome: | The proposed model lacks representativeness due to location-based, socio-demographic and register variation in CSW data. |