Search Results for author: David R. Mortensen

Found 31 papers, 11 papers with code

A Hmong Corpus with Elaborate Expression Annotations

no code implementations LREC 2022 David R. Mortensen, Xinyu Zhang, Chenxuan Cui, Katherine Zhang

This paper describes the first publicly available corpus of Hmong, a minority language of China, Vietnam, Laos, Thailand, and various countries in Europe and the Americas.

Word Embeddings

WikiHan: A New Comparative Dataset for Chinese Languages

2 code implementations COLING 2022 Kalvin Chang, Chenxuan Cui, Youngmin Kim, David R. Mortensen

Most comparative datasets of Chinese varieties are not digital; however, Wiktionary includes a wealth of transcriptions of words from these varieties.

Phone Inventories and Recognition for Every Language

no code implementations LREC 2022 Xinjian Li, Florian Metze, David R. Mortensen, Alan W Black, Shinji Watanabe

Identifying phone inventories is a crucial component in language documentation and the preservation of endangered languages.

ChatGPT MT: Competitive for High- (but not Low-) Resource Languages

no code implementations14 Sep 2023 Nathaniel R. Robinson, Perez Ogayo, David R. Mortensen, Graham Neubig

Without published experimental evidence on the matter, it is difficult for speakers of the world's diverse languages to know how and whether they can use LLMs for their languages.

Machine Translation

Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models

no code implementations23 May 2023 Orevaoghene Ahia, Sachin Kumar, Hila Gonen, Jungo Kasai, David R. Mortensen, Noah A. Smith, Yulia Tsvetkov

Language models have graduated from being research prototypes to commercialized products offered as web APIs, and recent works have highlighted the multilingual capabilities of these products.

Fairness Language Modelling

Construction Grammar Provides Unique Insight into Neural Language Models

no code implementations4 Feb 2023 Leonie Weissweiler, Taiqi He, Naoki Otani, David R. Mortensen, Lori Levin, Hinrich Schütze

Construction Grammar (CxG) has recently been used as the basis for probing studies that have investigated the performance of large pretrained language models (PLMs) with respect to the structure and meaning of constructions.

Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican

no code implementations loresmt (COLING) 2022 Nathaniel R. Robinson, Cameron J. Hogan, Nancy Fulda, David R. Mortensen

Our experiments suggest that for some languages beyond a threshold of authentic data, back-translation augmentation methods are counterproductive, while cross-lingual transfer from a sufficiently related language is preferred.

Cross-Lingual Transfer Machine Translation +2

When Is TTS Augmentation Through a Pivot Language Useful?

1 code implementation20 Jul 2022 Nathaniel Robinson, Perez Ogayo, Swetha Gangu, David R. Mortensen, Shinji Watanabe

Developing Automatic Speech Recognition (ASR) for low-resource languages is a challenge due to the small amount of transcribed audio data.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinese

no code implementations NAACL 2022 Chenxuan Cui, Katherine J. Zhang, David R. Mortensen

Mortensen (2006) claims that (1) the linear ordering of EEs and CCs in Hmong, Lahu, and Chinese can be predicted via phonological hierarchies and (2) these phonological hierarchies lack a clear phonetic rationale.

Differentiable Allophone Graphs for Language-Universal Speech Recognition

1 code implementation24 Jul 2021 Brian Yan, Siddharth Dalmia, David R. Mortensen, Florian Metze, Shinji Watanabe

These phone-based systems with learned allophone graphs can be used by linguists to document new languages, build phone-based lexicons that capture rich pronunciation variations, and re-evaluate the allophone mappings of seen language.

speech-recognition Speech Recognition

Automatic Extraction of Rules Governing Morphological Agreement

1 code implementation EMNLP 2020 Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa, David R. Mortensen, Zaid Sheikh, Yulia Tsvetkov, Graham Neubig

Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data.

Cross-Lingual Transfer Descriptive

Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks

2 code implementations EACL 2021 Jimin Sun, Hwijeen Ahn, Chan Young Park, Yulia Tsvetkov, David R. Mortensen

Much work in cross-lingual transfer learning explored how to select better transfer languages for multilingual tasks, primarily focusing on typological and genealogical similarities between languages.

Cross-Lingual Transfer Dependency Parsing +2

Characterizing Sociolinguistic Variation in the Competing Vaccination Communities

no code implementations8 Jun 2020 Shahan Ali Memon, Aman Tyagi, David R. Mortensen, Kathleen M. Carley

For an effective health communication, it is imperative to focus on "preference-based framing" where the preferences of the target sub-community are taken into consideration.


Computerized Forward Reconstruction for Analysis in Diachronic Phonology, and Latin to French Reflex Prediction

no code implementations LREC 2020 Clayton Marr, David R. Mortensen

Traditionally, historical phonologists have relied on tedious manual derivations to calibrate the sequences of sound changes that shaped the phonological evolution of languages.

AlloVera: A Multilingual Allophone Database

no code implementations LREC 2020 David R. Mortensen, Xinjian Li, Patrick Littell, Alexis Michaud, Shruti Rijhwani, Antonios Anastasopoulos, Alan W. black, Florian Metze, Graham Neubig

While phonemic representations are language specific, phonetic representations (stated in terms of (allo)phones) are much closer to a universal (language-independent) transcription.

speech-recognition Speech Recognition

Towards Zero-shot Learning for Automatic Phonemic Transcription

no code implementations26 Feb 2020 Xinjian Li, Siddharth Dalmia, David R. Mortensen, Juncheng Li, Alan W. black, Florian Metze

The difficulty of this task is that phoneme inventories often differ between the training languages and the target language, making it infeasible to recognize unseen phonemes.

Zero-Shot Learning

Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods

1 code implementation SCiL 2020 Maria Ryskina, Ella Rabinovich, Taylor Berg-Kirkpatrick, David R. Mortensen, Yulia Tsvetkov

Besides presenting a new linguistic application of distributional semantics, this study tackles the linguistic question of the role of language-internal factors (in our case, sparsity) in language change motivated by language-external factors (reflected in frequency growth).

Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation

no code implementations7 Nov 2019 Zhong Zhou, Lori Levin, David R. Mortensen, Alex Waibel

Firstly, we pool IGT for 1, 497 languages in ODIN (54, 545 glosses) and 70, 918 glosses in Arapaho and train a gloss-to-target NMT system from IGT to English, with a BLEU score of 25. 94.

Machine Translation NMT +1

The ARIEL-CMU Systems for LoReHLT18

no code implementations24 Feb 2019 Aditi Chaudhary, Siddharth Dalmia, Junjie Hu, Xinjian Li, Austin Matthews, Aldrian Obaja Muis, Naoki Otani, Shruti Rijhwani, Zaid Sheikh, Nidhi Vyas, Xinyi Wang, Jiateng Xie, Ruochen Xu, Chunting Zhou, Peter J. Jansen, Yiming Yang, Lori Levin, Florian Metze, Teruko Mitamura, David R. Mortensen, Graham Neubig, Eduard Hovy, Alan W. black, Jaime Carbonell, Graham V. Horwood, Shabnam Tafreshi, Mona Diab, Efsun S. Kayi, Noura Farra, Kathleen McKeown

This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).

Machine Translation Translation

URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors

no code implementations EACL 2017 Patrick Littell, David R. Mortensen, Ke Lin, Katherine Kairis, Carlisle Turner, Lori Levin

We introduce the URIEL knowledge base for massively multilingual NLP and the lang2vec utility, which provides information-rich vector identifications of languages drawn from typological, geographical, and phylogenetic databases and normalized to have straightforward and consistent formats, naming, and semantics.

Language Identification Language Modelling +1

Named Entity Recognition for Linguistic Rapid Response in Low-Resource Languages: Sorani Kurdish and Tajik

no code implementations COLING 2016 Patrick Littell, Kartik Goyal, David R. Mortensen, Alexa Little, Chris Dyer, Lori Levin

This paper describes our construction of named-entity recognition (NER) systems in two Western Iranian languages, Sorani Kurdish and Tajik, as a part of a pilot study of {``}Linguistic Rapid Response{''} to potential emergency humanitarian relief situations.

Humanitarian named-entity-recognition +2

PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors

1 code implementation COLING 2016 David R. Mortensen, Patrick Littell, Akash Bharadwaj, Kartik Goyal, Chris Dyer, Lori Levin

This paper contributes to a growing body of evidence that{---}when coupled with appropriate machine-learning techniques{--}linguistically motivated, information-rich representations can outperform one-hot encodings of linguistic data.


Bridge-Language Capitalization Inference in Western Iranian: Sorani, Kurmanji, Zazaki, and Tajik

no code implementations LREC 2016 Patrick Littell, David R. Mortensen, Kartik Goyal, Chris Dyer, Lori Levin

In Sorani Kurdish, one of the most useful orthographic features in named-entity recognition {--} capitalization {--} is absent, as the language{'}s Perso-Arabic script does not make a distinction between uppercase and lowercase letters.

named-entity-recognition Named Entity Recognition +1

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