Search Results for author:
Found 46 papers, 5 papers with code
Orthographic vs. Semantic Representations for Unsupervised Morphological Paradigm Clustering
This paper presents two different systems for unsupervised clustering of morphological paradigms, in the context of the SIGMORPHON 2021 Shared Task 2.
Modal Sense Classification At Large: Paraphrase-Driven Sense Projection, Semantically Enriched Classification Models and Cross-Genre Evaluations
Modal verbs have different interpretations depending on their context.
Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas
This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas.
Contrast Sets for Stativity of English Verbs in Context
For the task of classifying verbs in context as dynamic or stative, current models approach human performance, but only for particular data sets.
Machine Translation Between High-resource Languages in a Language Documentation Setting
This translation task is challenging for multiple reasons: (1) the data is out-of-domain with respect to the MT system’s training data, (2) much of the data is conversational, (3) existing translations include non-standard and uncommon expressions, often reflecting properties of the documented language, and (4) the data includes borrowings from other regional languages.
From Priest to Doctor: Domain Adaptaion for Low-Resource Neural Machine Translation
Many of the world's languages have insufficient data to train high-performing general neural machine translation (NMT) models, let alone domain-specific models, and often the only available parallel data are small amounts of religious texts.
Domain Adaptation
Low Resource Neural Machine Translation
+3
Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation
In this paper, we propose a retrieval augmented generation (RAG) framework backed by a large language model (LLM) to correct the output of a smaller model for the linguistic task of morphological glossing.
Can we teach language models to gloss endangered languages?
We explore whether LLMs can be effective at the task of interlinear glossing with in-context learning, without any traditional training.
TAMS: Translation-Assisted Morphological Segmentation
Canonical morphological segmentation is the process of analyzing words into the standard (aka underlying) forms of their constituent morphemes.
GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text
We compile the largest existing corpus of IGT data from a variety of sources, covering over 450k examples across 1. 8k languages, to enable research on crosslingual transfer and IGT generation.
Robust Generalization Strategies for Morpheme Glossing in an Endangered Language Documentation Context
Generalization is of particular importance in resource-constrained settings, where the available training data may represent only a small fraction of the distribution of possible texts.
Taxonomic Loss for Morphological Glossing of Low-Resource Languages
Morpheme glossing is a critical task in automated language documentation and can benefit other downstream applications greatly.
System Demo: Tool and Infrastructure for Offensive Language Error Analysis (OLEA) in English
The automatic detection of offensive language is a pressing societal need.
A Kind Introduction to Lexical and Grammatical Aspect, with a Survey of Computational Approaches
This includes whether a situation is described as a state or as an event, whether the situation is finished or ongoing, and whether it is viewed as a whole or with a focus on a particular phase.
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
Continued pretraining offers improvements, with an average accuracy of 44. 05%.
UNT Linguistics at SemEval-2020 Task 12: Linear SVC with Pre-trained Word Embeddings as Document Vectors and Targeted Linguistic Features
This paper outlines our approach to Tasks A {\&} B for the English Language track of SemEval-2020 Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media.
UNTLing at SemEval-2020 Task 11: Detection of Propaganda Techniques in English News Articles
Our system for the PropEval task explores the ability of semantic features to detect and label propagandistic rhetorical techniques in English news articles.
It's not a Non-Issue: Negation as a Source of Error in Machine Translation
As machine translation (MT) systems progress at a rapid pace, questions of their adequacy linger.
Predicting the Focus of Negation: Model and Error Analysis
The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances.
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts
Specifically, to complete the full TOP task for a given article, a system must do the following: a) identify possessors; b) anchor possessors to times/events; c) identify temporal relations between each temporal anchor and the possession relation it corresponds to; d) assign certainty scores to each possessor and each temporal relation; and e) assemble individual possession events into a global possession timeline.
A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited.
Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few
While our system results are dramatically below the average system submitted for the shared task evaluation campaign, our method is (we suspect) unique in its minimal reliance on labeled training data.
A Corpus of Negations and their Underlying Positive Interpretations
Negation often conveys implicit positive meaning.
Determining Event Durations: Models and Error Analysis
This paper presents models to predict event durations.
Modeling Communicative Purpose with Functional Style: Corpus and Features for German Genre and Register Analysis
The feature sets are used for supervised text genre classification, on which our models achieve high accuracy.
Illegal is not a Noun: Linguistic Form for Detection of Pejorative Nominalizations
This paper focuses on a particular type of abusive language, targeting expressions in which typically neutral adjectives take on pejorative meaning when used as nouns - compare {`}gay people{'} to {`}the gays{'}.
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention
Detecting aspectual properties of clauses in the form of situation entity types has been shown to depend on a combination of syntactic-semantic and contextual features.
Finding a Tradeoff between Accuracy and Rater's Workload in Grading Clustered Short Answers
n this paper we investigate the potential of answer clustering for semi-automatic scoring of short answer questions for German as a foreign language.
LQVSumm: A Corpus of Linguistic Quality Violations in Multi-Document Summarization
We present LQVSumm, a corpus of about 2000 automatically created extractive multi-document summaries from the TAC 2011 shared task on Guided Summarization, which we annotated with several types of linguistic quality violations.
