Sebastian Nehrdich

Sebastian Nehrdich

Research @ BAIR, UC Berkeley; PhD candidate @ University of Duesseldorf

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 2

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Blog Post number 1

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portfolio

publications

Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks

Published in Conference on Empirical Methods in Natural Language Processing, 2018

This paper presents end-to-end neural network models for Sanskrit tokenization, jointly handling compound splitting and Sandhi resolution. The language-agnostic models outperform previous approaches for Sanskrit and also excel in German compound splitting.

Recommended citation: Hellwig, O., & Nehrdich, S. (2018). "Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks." In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).
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A Method for the Calculation of Parallel Passages for Buddhist Chinese Sources Based on Million-scale Nearest Neighbor Search

Published in Journal of the Japanese Association for Digital Humanities, 2020

This paper introduces a novel approach to detect parallel passages in the Chinese Buddhist canon using continuous word representations and nearest neighbor search. It evaluates the quality of detected parallels and demonstrates a web application for philological research.

Recommended citation: Nehrdich, S. (2020). "A Method for the Calculation of Parallel Passages for Buddhist Chinese Sources Based on Million-scale Nearest Neighbor Search." Journal of the Japanese Association for Digital Humanities, 5(2), 132-153.
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Obtaining More Expressive Corpus Distributions for Standardized Ancient Languages

Published in CHR 2021: Computational Humanities Research Conference, 2021

This paper introduces a latent variable model for ancient languages to quantify the influence of early authoritative works on their literary successors in terms of lexis. The model jointly estimates word reuse and composition dates, applied to a corpus of pre-Renaissance Latin texts.

Recommended citation: Hellwig, O., Sellmer, S., & Nehrdich, S. (2021). "Obtaining More Expressive Corpus Distributions for Standardized Ancient Languages." In Proceedings of the Computational Humanities Research Conference (CHR 2021), Amsterdam, The Netherlands.
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Accurate Dependency Parsing and Tagging of Latin

Published in Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages, 2022

This paper explores the use of Latin BERT word embeddings for morpho-syntactic tagging and introduces a graph-based dependency parser for Latin. The proposed models show competitive performance in tagging and outperform various baselines in parsing.

Recommended citation: Nehrdich, S., & Hellwig, O. (2022). "Accurate Dependency Parsing and Tagging of Latin." In Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages, pages 20-25, Marseille, France. European Language Resources Association.
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SansTib, a Sanskrit - Tibetan Parallel Corpus and Bilingual Sentence Embedding Model

Published in Proceedings of the Thirteenth Language Resources and Evaluation Conference, 2022

This paper introduces SansTib, a large-scale Sanskrit-Classical Tibetan parallel corpus with 317,289 automatically aligned sentence pairs. It also presents a bilingual sentence embedding model and evaluates the quality of the automatic alignment using a gold evaluation dataset.

Recommended citation: Nehrdich, S. (2022). "SansTib, a Sanskrit - Tibetan Parallel Corpus and Bilingual Sentence Embedding Model." In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6728-6734, Marseille, France. European Language Resources Association.
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Data-driven dependency parsing of Vedic Sanskrit

Published in Language Resources and Evaluation, 2023

This paper introduces the first data-driven parser for Vedic Sanskrit, exploring various input feature representations and analyzing parsing errors. The optimal model achieves 87.61 unlabeled and 81.84 labeled attachment scores, demonstrating good performance for this under-resourced ancient Indo-Aryan language.

Recommended citation: Hellwig, O., Nehrdich, S., & Sellmer, S. (2023). "Data-driven dependency parsing of Vedic Sanskrit." Language Resources and Evaluation, 57, 1173-1206.
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MITRA-zh: An efficient, open machine translation solution for Buddhist Chinese

Published in NLP4DH, 2023

This paper presents a novel dataset and fine-tuned models for machine translation of Buddhist Classical Chinese, outperforming commercial solutions in efficiency and performance.

Recommended citation: Nehrdich, S., Bingenheimer, M., Brody, J., & Keutzer, K. (2023). "MITRA-zh: An efficient, open machine translation solution for Buddhist Chinese." NLP4DH.
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Observations on the Intertextuality of Selected Abhidharma Texts Preserved in Chinese Translation

Published in Religions, 2023

This study applies computer-aided methods to detect textual reuse in Xuanzang’s translation corpus and selected Abhidharma texts in Chinese. It presents network graph visualizations and examines reuse patterns, demonstrating alignment with established scholarship and providing a foundation for future detailed studies.

Recommended citation: Nehrdich, S. (2023). "Observations on the Intertextuality of Selected Abhidharma Texts Preserved in Chinese Translation." Religions, 14(7), 911.
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One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks

Published in The 2024 Conference on Empirical Methods in Natural Language Processing (Findings), 2024

This paper introduces ByT5-Sanskrit, a new pretrained language model for Sanskrit NLP tasks, demonstrating superior performance in word segmentation, dependency parsing, and OCR post-correction, while also introducing a novel multitask dataset.

Recommended citation: Nehrdich, S., Hellwig, O., & Keutzer, K. (2024). "One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (Findings).
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Breakthroughs in Tibetan NLP & Digital Humanities

Published in Revue d'Etudes Tibétaines, 2024

This paper discusses recent advancements in Tibetan Natural Language Processing and Digital Humanities.

Recommended citation: Meelen, M., Nehrdich, S., & Keutzer, K. (2024). "Breakthroughs in Tibetan NLP & Digital Humanities." Revue d'Etudes Tibétaines. 72, 5-25.
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talks

MITRA: Developing Language Models for Machine Translation and Search in Buddhist Source Languages

Published:

Translation and search are among the fundamental problems when researching the textual source material of Buddhist traditions. MITRA has successfully developed machine translation models to ease the access to this material. When it comes to search, The Dharmamitra project approaches this problem by using semantic embeddings that enable search on related passages in different languages, regardless of whether the answer to the query is found in a text preserved in Pāli, Sanskrit, Tibetan, or Chinese. In addition to providing researchers with this powerful search system, Dharmamitra also provides a system for the automatic detection of similar text passages within the same language and across different languages. In my talk, I will demonstrate how these tools are designed and how researchers can access them and integrate them in their workflow.

MITRA: Beyond Just Machine Translation for Premodern Asian Low Resource Languages

Published:

Recent years saw the rise of multilingual language models that achieve high levels of performance for a large number of tasks, with some of them handling hundreds of languages at once. Premodern languages are usually underrepresented in such models, leading to poor performance in downstream applications. The Dharmamitra project aims to develop a diverse set of language models to address these shortcomings for the classical Asian low-resource languages Sanskrit, Tibetan, Classical Chinese, and Pali. These models provide solutions for low-level NLP tasks such as word segmentation and morpho-syntactic tagging, as well as high-level tasks including semantic search, machine translation, and general chatbot interaction. The talk will address the individual challenges and unique characteristics of the data involved, and the strategies deployed to address these. It will also demonstrate how these different tools can be combined in an application that goes beyond simple sentence-to-sentence machine translation, providing detailed grammatical explanations and corpus-wide search to support both early-stage language learners and experienced researchers with specific demands.

MITRASearch: Building Information Retrieval Systems for Classical Asian Languages in the Age of AI

Published:

Recent advances in artificial intelligence and natural language processing have revolutionized information retrieval and question-answering systems. This talk introduces MITRASearch, a specialized search platform designed for exploring Buddhist literature preserved across Classical Asian languages including Chinese, Tibetan, Sanskrit, and Pāli. The system leverages multilingual approximate search capabilities to enable scholars to identify parallel passages and conduct comparative analyses across different writing systems and translations. We demonstrate how large language models integrated into the Dharmamitra project enhance user interaction with search results, facilitating dynamic exploration of these classical texts. This innovation addresses the long-standing challenge of cross-linguistic textual research in Buddhist studies and offers new possibilities for digital humanities scholarship.

Machine Translation for Asian Studies

Published:

With the advent of large language models, machine translation (MT) has become a widely used, but little understood, tool for research, language learning, and communication. GPT, Claude, and many other model series allow researchers now to access literature in different languages, and even translate primary texts composed in classical languages with few resources available. But how to evaluate the translation output of such machines? How to decide which model is the best for my own research purposes and how to tweak it? How will MT impact language learning, which is fundamental for Asian Studies?

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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