Sarah Moeller

Also published as: Sarah R. Moeller


2024

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A Comparison of Fine-Tuning and In-Context Learning for Clause-Level Morphosyntactic Alternation
Jim Su | Justin Ho | George Broadwell | Sarah Moeller | Bonnie Dorr
Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)

This paper presents our submission to the AmericasNLP 2024 Shared Task on the Creation of Educational Materials for Indigenous Languages. We frame this task as one of morphological inflection generation, treating each sentence as a single word. We investigate and compare two distinct approaches: fine-tuning neural encoder-decoder models such as NLLB- 200, and in-context learning with proprietary large language models (LLMs). Our findings demonstrate that for this task, no one approach is perfect. Anthropic’s Claude 3 Opus, when supplied with grammatical description entries, achieves the highest performance on Bribri among the evaluated models. This outcome corroborates and extends previous research exploring the efficacy of in-context learning in low- resource settings. For Maya, fine-tuning NLLB- 200-3.3B using StemCorrupt augmented data yielded the best performance.

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Proceedings of the Seventh Workshop on the Use of Computational Methods in the Study of Endangered Languages
Sarah Moeller | Godfred Agyapong | Antti Arppe | Aditi Chaudhary | Shruti Rijhwani | Christopher Cox | Ryan Henke | Alexis Palmer | Daisy Rosenblum | Lane Schwartz
Proceedings of the Seventh Workshop on the Use of Computational Methods in the Study of Endangered Languages

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Machine-in-the-Loop with Documentary and Descriptive Linguists
Sarah Moeller | Antti Arppe
Proceedings of the Seventh Workshop on the Use of Computational Methods in the Study of Endangered Languages

This paper describes a curriculum for teaching linguists how to apply machine-in-the-loop (MitL) approach to documentary and descriptive tasks. It also shares observations about the learning participants, who are primarily non-computational linguists, and how they interact with the MitL approach. We found that they prefer cleaning over increasing the training data and then proceed to reanalyze their analytical decisions, before finally undertaking small actions that emphasize analytical strategies. Overall, participants display an understanding of the curriculum which covers fundamental concepts of machine learning and statistical modeling.

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Leveraging Syntactic Dependencies in Disambiguation: The Case of African American English
Wilermine Previlon | Alice Rozet | Jotsna Gowda | Bill Dyer | Kevin Tang | Sarah Moeller
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

African American English (AAE) has received recent attention in the field of natural language processing (NLP). Efforts to address bias against AAE in NLP systems tend to focus on lexical differences. When the unique structures of AAE are considered, the solution is often to remove or neutralize the differences. This work leverages knowledge about the unique linguistic structures to improve automatic disambiguation of habitual and non-habitual meanings of “be” in naturally produced AAE transcribed speech. Both meanings are employed in AAE but examples of Habitual be are rare in already limited AAE data. Generally, representing additional syntactic information improves semantic disambiguation of habituality. Using an ensemble of classical machine learning models with a representation of the unique POS and dependency patterns of Habitual be, we show that integrating syntactic information improves the identification of habitual uses of “be” by about 65 F1 points over a simple baseline model of n-grams, and as much as 74 points. The success of this approach demonstrates the potential impact when we embrace, rather than neutralize, the structural uniqueness of African American English.

2023

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Proceedings of the Sixth Workshop on the Use of Computational Methods in the Study of Endangered Languages
Atticus Harrigan | Aditi Chaudhary | Shruti Rijhwani | Sarah Moeller | Antti Arppe | Alexis Palmer | Ryan Henke | Daisy Rosenblum
Proceedings of the Sixth Workshop on the Use of Computational Methods in the Study of Endangered Languages

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Morphological Data Generation from FLEx
Shengyu Liao | Sarah Moeller | Beth Bryson
Proceedings of the Sixth Workshop on the Use of Computational Methods in the Study of Endangered Languages

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Findings of the SIGMORPHON 2023 Shared Task on Interlinear Glossing
Michael Ginn | Sarah Moeller | Alexis Palmer | Anna Stacey | Garrett Nicolai | Mans Hulden | Miikka Silfverberg
Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology

This paper presents the findings of the SIGMORPHON 2023 Shared Task on Interlinear Glossing. This first iteration of the shared task explores glossing of a set of six typologically diverse languages: Arapaho, Gitksan, Lezgi, Natügu, Tsez and Uspanteko. The shared task encompasses two tracks: a resource-scarce closed track and an open track, where participants are allowed to utilize external data resources. Five teams participated in the shared task. The winning team Tü-CL achieved a 23.99%-point improvement over a baseline RoBERTa system in the closed track and a 17.42%-point improvement in the open track.

2022

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Disambiguation of morpho-syntactic features of African American English – the case of habitual be
Harrison Santiago | Joshua Martin | Sarah Moeller | Kevin Tang
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

Recent research has highlighted that natural language processing (NLP) systems exhibit a bias againstAfrican American speakers. These errors are often caused by poor representation of linguistic features unique to African American English (AAE), which is due to the relatively low probability of occurrence for many such features. We present a workflow to overcome this issue in the case of habitual “be”. Habitual “be” is isomorphic, and therefore ambiguous, with other forms of uninflected “be” found in both AAE and General American English (GAE). This creates a clear challenge for bias in NLP technologies. To overcome the scarcity, we employ a combination of rule-based filters and data augmentation that generate a corpus balanced between habitual and non-habitual instances. This balanced corpus trains unbiased machine learning classifiers, as demonstrated on a corpus of AAE transcribed texts, achieving .65 F1 score at classifying habitual “be”.

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Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages
Sarah Moeller | Antonios Anastasopoulos | Antti Arppe | Aditi Chaudhary | Atticus Harrigan | Josh Holden | Jordan Lachler | Alexis Palmer | Shruti Rijhwani | Lane Schwartz
Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages

2021

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To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings
Sarah Moeller | Ling Liu | Mans Hulden
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Part-of-Speech (POS) tags are routinely included as features in many NLP tasks. However, the importance and usefulness of POS tags needs to be examined as NLP expands to low-resource languages because linguists who provide many annotated resources do not place priority on early identification and tagging of POS. This paper describes an empirical study about the effect that POS tags have on two computational morphological tasks with the Transformer architecture. Each task is tested twice on identical data except for the presence/absence of POS tags, using published data in ten high- to low-resource languages or unpublished linguistic field data in five low-resource languages. We find that the presence or absence of POS tags does not have a significant bearing on performance. In joint segmentation and glossing, the largest average difference is an .09 improvement in F1-scores by removing POS tags. In reinflection, the greatest average difference is 1.2% in accuracy for published data and 5% for unpublished and noisy field data.

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Proceedings of the 4th Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)
Antti Arppe | Jeff Good | Atticus Harrigan | Mans Hulden | Jordan Lachler | Sarah Moeller | Alexis Palmer | Miikka Silfverberg | Lane Schwartz
Proceedings of the 4th Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

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Integrating Automated Segmentation and Glossing into Documentary and Descriptive Linguistics
Sarah Moeller | Mans Hulden
Proceedings of the 4th Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

2020

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The Russian PropBank
Sarah Moeller | Irina Wagner | Martha Palmer | Kathryn Conger | Skatje Myers
Proceedings of the Twelfth Language Resources and Evaluation Conference

This paper presents a proposition bank for Russian (RuPB), a resource for semantic role labeling (SRL). The motivating goal for this resource is to automatically project semantic role labels from English to Russian. This paper describes frame creation strategies, coverage, and the process of sense disambiguation. It discusses language-specific issues that complicated the process of building the PropBank and how these challenges were exploited as language-internal guidance for consistency and coherence.

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A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Graham Neubig | Shruti Rijhwani | Alexis Palmer | Jordan MacKenzie | Hilaria Cruz | Xinjian Li | Matthew Lee | Aditi Chaudhary | Luke Gessler | Steven Abney | Shirley Anugrah Hayati | Antonios Anastasopoulos | Olga Zamaraeva | Emily Prud’hommeaux | Jennette Child | Sara Child | Rebecca Knowles | Sarah Moeller | Jeffrey Micher | Yiyuan Li | Sydney Zink | Mengzhou Xia | Roshan Sharma | Patrick Littell
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited. In August 2019, a workshop was held at Carnegie Mellon University in Pittsburgh, PA, USA to attempt to bring together language community members, documentary linguists, and technologists to discuss how to bridge this gap and create prototypes of novel and practical language revitalization technologies. The workshop focused on developing technologies to aid language documentation and revitalization in four areas: 1) spoken language (speech transcription, phone to orthography decoding, text-to-speech and text-speech forced alignment), 2) dictionary extraction and management, 3) search tools for corpora, and 4) social media (language learning bots and social media analysis). This paper reports the results of this workshop, including issues discussed, and various conceived and implemented technologies for nine languages: Arapaho, Cayuga, Inuktitut, Irish Gaelic, Kidaw’ida, Kwak’wala, Ojibwe, San Juan Quiahije Chatino, and Seneca.

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IGT2P: From Interlinear Glossed Texts to Paradigms
Sarah Moeller | Ling Liu | Changbing Yang | Katharina Kann | Mans Hulden
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

An intermediate step in the linguistic analysis of an under-documented language is to find and organize inflected forms that are attested in natural speech. From this data, linguists generate unseen inflected word forms in order to test hypotheses about the language’s inflectional patterns and to complete inflectional paradigm tables. To get the data linguists spend many hours manually creating interlinear glossed texts (IGTs). We introduce a new task that speeds this process and automatically generates new morphological resources for natural language processing systems: IGT-to-paradigms (IGT2P). IGT2P generates entire morphological paradigms from IGT input. We show that existing morphological reinflection models can solve the task with 21% to 64% accuracy, depending on the language. We further find that (i) having a language expert spend only a few hours cleaning the noisy IGT data improves performance by as much as 21 percentage points, and (ii) POS tags, which are generally considered a necessary part of NLP morphological reinflection input, have no effect on the accuracy of the models considered here.

2019

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Linguistic Analysis Improves Neural Metaphor Detection
Kevin Stowe | Sarah Moeller | Laura Michaelis | Martha Palmer
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

In the field of metaphor detection, deep learning systems are the ubiquitous and achieve strong performance on many tasks. However, due to the complicated procedures for manually identifying metaphors, the datasets available are relatively small and fraught with complications. We show that using syntactic features and lexical resources can automatically provide additional high-quality training data for metaphoric language, and this data can cover gaps and inconsistencies in metaphor annotation, improving state-of-the-art word-level metaphor identification. This novel application of automatically improving training data improves classification across numerous tasks, and reconfirms the necessity of high-quality data for deep learning frameworks.

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Improving Low-Resource Morphological Learning with Intermediate Forms from Finite State Transducers
Sarah Moeller | Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

2018

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Morphological Reinflection in Context: CU Boulder’s Submission to CoNLLSIGMORPHON 2018 Shared Task
Ling Liu | Ilamvazhuthy Subbiah | Adam Wiemerslage | Jonathan Lilley | Sarah Moeller
Proceedings of the CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection

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Comprehensive Supersense Disambiguation of English Prepositions and Possessives
Nathan Schneider | Jena D. Hwang | Vivek Srikumar | Jakob Prange | Austin Blodgett | Sarah R. Moeller | Aviram Stern | Adi Bitan | Omri Abend
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Semantic relations are often signaled with prepositional or possessive marking—but extreme polysemy bedevils their analysis and automatic interpretation. We introduce a new annotation scheme, corpus, and task for the disambiguation of prepositions and possessives in English. Unlike previous approaches, our annotations are comprehensive with respect to types and tokens of these markers; use broadly applicable supersense classes rather than fine-grained dictionary definitions; unite prepositions and possessives under the same class inventory; and distinguish between a marker’s lexical contribution and the role it marks in the context of a predicate or scene. Strong interannotator agreement rates, as well as encouraging disambiguation results with established supervised methods, speak to the viability of the scheme and task.

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A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer
Sarah Moeller | Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages

We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68% (unambiguous verbs) and 92.90% (all verbs).

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Automatic Glossing in a Low-Resource Setting for Language Documentation
Sarah Moeller | Mans Hulden
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages

Morphological analysis of morphologically rich and low-resource languages is important to both descriptive linguistics and natural language processing. Field documentary efforts usually procure analyzed data in cooperation with native speakers who are capable of providing some level of linguistic information. Manually annotating such data is very expensive and the traditional process is arguably too slow in the face of language endangerment and loss. We report on a case study of learning to automatically gloss a Nakh-Daghestanian language, Lezgi, from a very small amount of seed data. We compare a conditional random field based sequence labeler and a neural encoder-decoder model and show that a nearly 0.9 F1-score on labeled accuracy of morphemes can be achieved with 3,000 words of transcribed oral text. Errors are mostly limited to morphemes with high allomorphy. These results are potentially useful for developing rapid annotation and fieldwork tools to support documentation of morphologically rich, endangered languages.