pwc-archive/papers-with-abstracts huggingface dataset:
Morphological Segmentation for Seneca
Authors: Zoey Liu, Robert Jimerson, Emily Prud’hommeaux
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Decoder, Model Selection, Multi-Task Learning, Segmentation

This study takes up the task of low-resource morphological segmentation for Seneca, a critically endangered and morphologically complex Native American language primarily spoken in what is now New York State and Ontario. The labeled data in our experiments comes from two sources: one digitized from a publicly available grammar book and the other collected from informal sources. We treat these two sources as distinct domains and investigate different evaluation designs for model selection. The first design abides by standard practices and evaluate models with the in-domain development set, while the second one carries out evaluation using a development domain, or the out-of-domain development set. Across a series of monolingual and crosslinguistic training settings, our results demonstrate the utility of neural encoder-decoder architecture when coupled with multi-task learning.
Representation of Yine [Arawak] Morphology by Finite State Transducer Formalism
Authors: Adriano Ingunza Torres, John Miller, Arturo Oncevay, Roberto Zariquiey Biondi
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6

We represent the complexity of Yine (Arawak) morphology with a finite state transducer (FST) based morphological analyzer. Yine is a low-resource indigenous polysynthetic Peruvian language spoken by approximately 3,000 people and is classified as ‘definitely endangered’ by UNESCO. We review Yine morphology focusing on morphophonology, possessive constructions and verbal predicates. Then we develop FSTs to model these components proposing techniques to solve challenging problems such as complex patterns of incorporating open and closed category arguments. This is a work in progress and we still have more to do in the development and verification of our analyzer. Our analyzer will serve both as a tool to better document the Yine language and as a component of natural language processing (NLP) applications such as spell checking and correction.
Leveraging English Word Embeddings for Semi-Automatic Semantic Classification in Nêhiyawêwin (Plains Cree)
Authors: Atticus Harrigan, Antti Arppe
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Classification, Clustering, Word Embeddings

This paper details a semi-automatic method of word clustering for the Algonquian language, Nêhiyawêwin (Plains Cree). Although this method worked well, particularly for nouns, it required some amount of manual postprocessing. The main benefit of this approach over implementing an existing classification ontology is that this method approaches the language from an endogenous point of view, while performing classification quicker than in a fully manual context.
Restoring the Sister: Reconstructing a Lexicon from Sister Languages using Neural Machine Translation
Authors: Remo Nitschke
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: BIG-bench Machine Learning, Decoder, Machine Translation, NMT, Translation

The historical comparative method has a long history in historical linguists. It describes a process by which historical linguists aim to reverse-engineer the historical developments of language families in order to reconstruct proto-forms and familial relations between languages. In recent years, there have been multiple attempts to replicate this process through machine learning, especially in the realm of cognate detection (List et al., 2016; Ciobanu and Dinu, 2014; Rama et al., 2018). So far, most of these experiments aimed at actual reconstruction have attempted the prediction of a proto-form from the forms of the daughter languages (Ciobanu and Dinu, 2018; Meloni et al., 2019).. Here, we propose a reimplementation that uses modern related languages, or sisters, instead, to reconstruct the vocabulary of a target language. In particular, we show that we can reconstruct vocabulary of a target language by using a fairly small data set of parallel cognates from different sister languages, using a neural machine translation (NMT) architecture with a standard encoder-decoder setup. This effort is directly in furtherance of the goal to use machine learning tools to help under-served language communities in their efforts at reclaiming, preserving, or reconstructing their own languages.
Expanding Universal Dependencies for Polysynthetic Languages: A Case of St. Lawrence Island Yupik
Authors: Hyunji Park, Lane Schwartz, Francis Tyers
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Dependency Parsing

This paper describes the development of the first Universal Dependencies (UD) treebank for St. Lawrence Island Yupik, an endangered language spoken in the Bering Strait region. While the UD guidelines provided a general framework for our annotations, language-specific decisions were made necessary by the rich morphology of the polysynthetic language. Most notably, we annotated a corpus at the morpheme level as well as the word level. The morpheme level annotation was conducted using an existing morphological analyzer and manual disambiguation. By comparing the two resulting annotation schemes, we argue that morpheme-level annotation is essential for polysynthetic languages like St. Lawrence Island Yupik. Word-level annotation results in degenerate trees for some Yupik sentences and often fails to capture syntactic relations that can be manifested at the morpheme level. Dependency parsing experiments provide further support for morpheme-level annotation. Implications for UD annotation of other polysynthetic languages are discussed.
The More Detail, the Better? – Investigating the Effects of Semantic Ontology Specificity on Vector Semantic Classification with a Plains Cree / nêhiyawêwin Dictionary
Authors: Daniel Dacanay, Atticus Harrigan, Arok Wolvengrey, Antti Arppe
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Specificity

One problem in the task of automatic semantic classification is the problem of determining the level on which to group lexical items. This is often accomplished using pre-made, hierarchical semantic ontologies. The following investigation explores the computational assignment of semantic classifications on the contents of a dictionary of nêhiyawêwin / Plains Cree (ISO: crk, Algonquian, Western Canada and United States), using a semantic vector space model, and following two semantic ontologies, WordNet and SIL’s Rapid Words, and compares how these computational results compare to manual classifications with the same two ontologies.
Experiments on a Guarani Corpus of News and Social Media
Authors: Santiago Góngora, Nicolás Giossa, Luis Chiruzzo
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Articles, Diversity, Word Embeddings

While Guarani is widely spoken in South America, obtaining a large amount of Guarani text from the web is hard. We present the building process of a Guarani corpus composed of a parallel Guarani-Spanish set of news articles, and a monolingual set of tweets. We perform some word embeddings experiments aiming at evaluating the quality of the Guarani split of the corpus, finding encouraging results but noticing that more diversity in text domains might be needed for further improvements.
Towards a First Automatic Unsupervised Morphological Segmentation for Inuinnaqtun
Authors: Ngoc Tan Le, Fatiha Sadat
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Machine Translation, Morphological Analysis, Translation

Low-resource polysynthetic languages pose many challenges in NLP tasks, such as morphological analysis and Machine Translation, due to available resources and tools, and the morphologically complex languages. This research focuses on the morphological segmentation while adapting an unsupervised approach based on Adaptor Grammars in low-resource setting. Experiments and evaluations on Inuinnaqtun, one of Inuit language family in Northern Canada, considered a language that will be extinct in less than two generations, have shown promising results.
Toward Creation of Ancash Lexical Resources from OCR
Authors: Johanna Cordova, Damien Nouvel
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Optical Character Recognition (OCR)

The Quechua linguistic family has a limited number of NLP resources, most of them being dedicated to Southern Quechua, whereas the varieties of Central Quechua have, to the best of our knowledge, no specific resources (software, lexicon or corpus). Our work addresses this issue by producing two resources for the Ancash Quechua: a full digital version of a dictionary, and an OCR model adapted to the considered variety. In this paper, we describe the steps towards this goal: we first measure performances of existing models for the task of digitising a Quechua dictionary, then adapt a model for the Ancash variety, and finally create a reliable resource for NLP in XML-TEI format. We hope that this work will be a basis for initiating NLP projects for Central Quechua, and that it will encourage digitisation initiatives for under-resourced languages.
Ayuuk-Spanish Neural Machine Translator
Authors: Delfino Zacarías Márquez, Ivan Vladimir Meza Ruiz
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6

This paper presents the first neural machine translator system for the Ayuuk language. In our experiments we translate from Ayuuk to Spanish, and fromSpanish to Ayuuk. Ayuuk is a language spoken in the Oaxaca state of Mexico by the Ayuukjä’äy people (in Spanish commonly known as Mixes. We use different sources to create a low-resource parallel corpus, more than 6,000 phrases. For some of these resources we rely on automatic alignment. The proposed system is based on the Transformer neural architecture and it uses sub-word level tokenization as the input. We show the current performance given the resources we have collected for the San Juan Güichicovi variant, they are promising, up to 5 BLEU. We based our development on the Masakhane project for African languages.
Explicit Tone Transcription Improves ASR Performance in Extremely Low-Resource Languages: A Case Study in Bribri
Authors: Rolando Coto-Solano
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6

Linguistic tone is transcribed for input into ASR systems in numerous ways. This paper shows a systematic test of several transcription styles, using as an example the Chibchan language Bribri, an extremely low-resource language from Costa Rica. The most successful models separate the tone from the vowel, so that the ASR algorithms learn tone patterns independently. These models showed improvements ranging from 4% to 25% in character error rate (CER), and between 3% and 23% in word error rate (WER). This is true for both traditional GMM/HMM and end-to-end CTC algorithms. This paper also presents the first attempt to train ASR models for Bribri. The best performing models had a CER of 33% and a WER of 50%. Despite the disadvantage of using hand-engineered representations, these models were trained on only 68 minutes of data, and therefore show the potential of ASR to generate further training materials and aid in the documentation and revitalization of the language.
Towards a morphological transducer and orthography converter for Western Tlacolula Valley Zapotec
Authors: Jonathan Washington, Felipe Lopez, Brook Lillehaugen
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6

This paper presents work towards a morphological transducer and orthography converter for Dizhsa, or San Lucas Quiaviní Zapotec, an endangered Western Tlacolula Valley Zapotec language. The implementation of various aspects of the language’s morphology is presented, as well as the transducer’s ability to perform analysis in two orthographies and convert between them. Potential uses of the transducer for language maintenance and issues of licensing are also discussed. Evaluation of the transducer shows that it is fairly robust although incomplete, and evaluation of orthographic conversion shows that this method is strongly affected by the coverage of the transducer.
Peru is Multilingual, Its Machine Translation Should Be Too?
Authors: Arturo Oncevay
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Machine Translation, Translation

Peru is a multilingual country with a long history of contact between the indigenous languages and Spanish. Taking advantage of this context for machine translation is possible with multilingual approaches for learning both unsupervised subword segmentation and neural machine translation models. The study proposes the first multilingual translation models for four languages spoken in Peru: Aymara, Ashaninka, Quechua and Shipibo-Konibo, providing both many-to-Spanish and Spanish-to-many models and outperforming pairwise baselines in most of them. The task exploited a large English-Spanish dataset for pre-training, monolingual texts with tagged back-translation, and parallel corpora aligned with English. Finally, by fine-tuning the best models, we also assessed the out-of-domain capabilities in two evaluation datasets for Quechua and a new one for Shipibo-Konibo.
Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas
Authors: Manuel Mager, Arturo Oncevay, Abteen Ebrahimi, John Ortega, Annette Rios, Angela Fan, Ximena Gutierrez-Vasques, Luis Chiruzzo, Gustavo Giménez-Lugo, Ricardo Ramos, Ivan Vladimir Meza Ruiz, Rolando Coto-Solano, Alexis Palmer, Elisabeth Mager-Hois, Vishrav Chaudhary, Graham Neubig, Ngoc Thang Vu, Katharina Kann
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Machine Translation, Translation

This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas. The shared task featured two independent tracks, and participants submitted machine translation systems for up to 10 indigenous languages. Overall, 8 teams participated with a total of 214 submissions. We provided training sets consisting of data collected from various sources, as well as manually translated sentences for the development and test sets. An official baseline trained on this data was also provided. Team submissions featured a variety of architectures, including both statistical and neural models, and for the majority of languages, many teams were able to considerably improve over the baseline. The best performing systems achieved 12.97 ChrF higher than baseline, when averaged across languages.
Open Machine Translation for Low Resource South American Languages (AmericasNLP 2021 Shared Task Contribution)
Authors: Shantipriya Parida, Subhadarshi Panda, Amulya Dash, Esau Villatoro-Tello, A. Seza Doğruöz, Rosa M. Ortega-Mendoza, Amadeo Hernández, Yashvardhan Sharma, Petr Motlicek
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Machine Translation, Translation

This paper describes the team (“Tamalli”)’s submission to AmericasNLP2021 shared task on Open Machine Translation for low resource South American languages. Our goal was to evaluate different Machine Translation (MT) techniques, statistical and neural-based, under several configuration settings. We obtained the second-best results for the language pairs “Spanish-Bribri”, “Spanish-Asháninka”, and “Spanish-Rarámuri” in the category “Development set not used for training”. Our performed experiments will serve as a point of reference for researchers working on MT with low-resource languages.
NRC-CNRC Machine Translation Systems for the 2021 AmericasNLP Shared Task
Authors: Rebecca Knowles, Darlene Stewart, Samuel Larkin, Patrick Littell
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Machine Translation, Translation

We describe the NRC-CNRC systems submitted to the AmericasNLP shared task on machine translation. We submitted systems translating from Spanish into Wixárika, Nahuatl, Rarámuri, and Guaraní. Our best neural machine translation systems used multilingual pretraining, ensembling, finetuning, training on parts of the development data, and subword regularization. We also submitted translation memory systems as a strong baseline.
Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining
Authors: Francis Zheng, Machel Reid, Edison Marrese-Taylor, Yutaka Matsuo
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

This paper describes UTokyo’s submission to the AmericasNLP 2021 Shared Task on machine translation systems for indigenous languages of the Americas. We present a low-resource machine translation system that improves translation accuracy using cross-lingual language model pretraining. Our system uses an mBART implementation of fairseq to pretrain on a large set of monolingual data from a diverse set of high-resource languages before finetuning on 10 low-resource indigenous American languages: Aymara, Bribri, Asháninka, Guaraní, Wixarika, Náhuatl, Hñähñu, Quechua, Shipibo-Konibo, and Rarámuri. On average, our system achieved BLEU scores that were 1.64 higher and chrF scores that were 0.0749 higher than the baseline.
The REPU CS’ Spanish–Quechua Submission to the AmericasNLP 2021 Shared Task on Open Machine Translation
Authors: Oscar Moreno
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Machine Translation, Translation

We present the submission of REPUcs to the AmericasNLP machine translation shared task for the low resource language pair Spanish–Quechua. Our neural machine translation system ranked first in Track two (development set not used for training) and third in Track one (training includes development data). Our contribution is focused on: (i) the collection of new parallel data from different web sources (poems, lyrics, lexicons, handbooks), and (ii) using large Spanish–English data for pre-training and then fine-tuning the Spanish–Quechua system. This paper describes the new parallel corpora and our approach in detail.
Moses and the Character-Based Random Babbling Baseline: CoAStaL at AmericasNLP 2021 Shared Task
Authors: Marcel Bollmann, Rahul Aralikatte, Héctor Murrieta Bello, Daniel Hershcovich, Miryam de Lhoneux, Anders Søgaard
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Machine Translation, Translation

We evaluated a range of neural machine translation techniques developed specifically for low-resource scenarios. Unsuccessfully. In the end, we submitted two runs: (i) a standard phrase-based model, and (ii) a random babbling baseline using character trigrams. We found that it was surprisingly hard to beat (i), in spite of this model being, in theory, a bad fit for polysynthetic languages; and more interestingly, that (ii) was better than several of the submitted systems, highlighting how difficult low-resource machine translation for polysynthetic languages is.
The Helsinki submission to the AmericasNLP shared task
Authors: Raúl Vázquez, Yves Scherrer, Sami Virpioja, Jörg Tiedemann
Date: 2021-06-01 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: NMT

The University of Helsinki participated in the AmericasNLP shared task for all ten language pairs. Our multilingual NMT models reached the first rank on all language pairs in track 1, and first rank on nine out of ten language pairs in track 2. We focused our efforts on three aspects: (1) the collection of additional data from various sources such as Bibles and political constitutions, (2) the cleaning and filtering of training data with the OpusFilter toolkit, and (3) different multilingual training techniques enabled by the latest version of the OpenNMT-py toolkit to make the most efficient use of the scarce data. This paper describes our efforts in detail.
Causal Augmentation for Causal Sentence Classification
Authors: Fiona Anting Tan, Devamanyu Hazarika, See-Kiong Ng, Soujanya Poria, Roger Zimmermann
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (CINLP) 2021 11
Tasks: Classification, counterfactual, Negation, Sentence, Sentence Classification

Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification. In particular, we found that models misclassify on augmented sentences that have been negated or strengthened with respect to its causal meaning. This is worrying since minor linguistic differences in causal sentences can have disparate meanings. Therefore, we propose the generation of counterfactual causal sentences by creating contrast sets (Gardner et al., 2020) to be included during model training. We experimented on two model architectures and predicted on two out-of-domain corpora. While our strengthening schemes proved useful in improving model performance, for negation, regular edits were insufficient. Thus, we also introduce heuristics like shortening or multiplying root words of a sentence. By including a mixture of edits when training, we achieved performance improvements beyond the baseline across both models, and within and out of corpus’ domain, suggesting that our proposed augmentation can also help models generalize.
Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization
Authors: Graham Tierney, Alexander Volfovsky
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (CINLP) 2021 11
Tasks: Dimensionality Reduction, Sensitivity

We introduce a procedure to examine a text-as-mediator problem from a novel randomized experiment that studied the effect of conversations on political polarization. In this randomized experiment, Americans from the Democratic and Republican parties were either randomly paired with one-another to have an anonymous conversation about politics or alternatively not assigned to a conversation — change in political polarization over time was measured for all participants. This paper analyzes the text of the conversations to identify potential mediators of depolarization and is faced with a unique challenge, necessitated by the primary research hypothesis, that individuals in the control condition do not have conversations and so lack observed text data. We highlight the importance of using domain knowledge to perform dimension reduction on the text data, and describe a procedure to characterize indirect effects via text when the text is only observed in one arm of the experiment.