Arya D. McCarthy

Also published as: Arya McCarthy


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Jump-Starting Item Parameters for Adaptive Language Tests
Arya D. McCarthy | Kevin P. Yancey | Geoffrey T. LaFlair | Jesse Egbert | Manqian Liao | Burr Settles
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

A challenge in designing high-stakes language assessments is calibrating the test item difficulties, either a priori or from limited pilot test data. While prior work has addressed ‘cold start’ estimation of item difficulties without piloting, we devise a multi-task generalized linear model with BERT features to jump-start these estimates, rapidly improving their quality with as few as 500 test-takers and a small sample of item exposures (6 each) from a large item bank (4,000 items). Our joint model provides a principled way to compare test-taker proficiency, item difficulty, and language proficiency frameworks like the Common European Framework of Reference (CEFR). This also enables new item difficulty estimates without piloting them first, which in turn limits item exposure and thus enhances test item security. Finally, using operational data from the Duolingo English Test, a high-stakes English proficiency test, we find that the difficulty estimates derived using this method correlate strongly with lexico-grammatical features that correlate with reading complexity.


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Unsupervised Morphological Paradigm Completion
Huiming Jin | Liwei Cai | Yihui Peng | Chen Xia | Arya McCarthy | Katharina Kann
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose the task of unsupervised morphological paradigm completion. Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all inflected forms, of the lemmas. From a natural language processing (NLP) perspective, this is a challenging unsupervised task, and high-performing systems have the potential to improve tools for low-resource languages or to assist linguistic annotators. From a cognitive science perspective, this can shed light on how children acquire morphological knowledge. We further introduce a system for the task, which generates morphological paradigms via the following steps : (i) EDIT TREE retrieval, (ii) additional lemma retrieval, (iii) paradigm size discovery, and (iv) inflection generation. We perform an evaluation on 14 typologically diverse languages. Our system outperforms trivial baselines with ease and, for some languages, even obtains a higher accuracy than minimally supervised systems.

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Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation
Arya D. McCarthy | Xian Li | Jiatao Gu | Ning Dong
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper proposes a simple and effective approach to address the problem of posterior collapse in conditional variational autoencoders (CVAEs). It thus improves performance of machine translation models that use noisy or monolingual data, as well as in conventional settings. Extending Transformer and conditional VAEs, our proposed latent variable model measurably prevents posterior collapse by (1) using a modified evidence lower bound (ELBO) objective which promotes mutual information between the latent variable and the target, and (2) guiding the latent variable with an auxiliary bag-of-words prediction task. As a result, the proposed model yields improved translation quality compared to existing variational NMT models on WMT RoEn and DeEn. With latent variables being effectively utilized, our model demonstrates improved robustness over non-latent Transformer in handling uncertainty : exploiting noisy source-side monolingual data (up to +3.2 BLEU), and training with weakly aligned web-mined parallel data (up to +4.7 BLEU).

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Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages
Garrett Nicolai | Dylan Lewis | Arya D. McCarthy | Aaron Mueller | Winston Wu | David Yarowsky
Proceedings of the 12th Language Resources and Evaluation Conference

Exploiting the broad translation of the Bible into the world’s languages, we train and distribute morphosyntactic tools for approximately one thousand languages, vastly outstripping previous distributions of tools devoted to the processing of inflectional morphology. Evaluation of the tools on a subset of available inflectional dictionaries demonstrates strong initial models, supplemented and improved through ensembling and dictionary-based reranking. Likewise, a novel type-to-token based evaluation metric allows us to confirm that models generalize well across rare and common forms alike

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Massively Multilingual Pronunciation Modeling with WikiPronWikiPron
Jackson L. Lee | Lucas F.E. Ashby | M. Elizabeth Garza | Yeonju Lee-Sikka | Sean Miller | Alan Wong | Arya D. McCarthy | Kyle Gorman
Proceedings of the 12th Language Resources and Evaluation Conference

We introduce WikiPron, an open-source command-line tool for extracting pronunciation data from Wiktionary, a collaborative multilingual online dictionary. We first describe the design and use of WikiPron. We then discuss the challenges faced scaling this tool to create an automatically-generated database of 1.7 million pronunciations from 165 languages. Finally, we validate the pronunciation database by using it to train and evaluating a collection of generic grapheme-to-phoneme models. The software, pronunciation data, and models are all made available under permissive open-source licenses.

The human unlikeness of neural language models in next-word prediction
Cassandra L. Jacobs | Arya D. McCarthy
Proceedings of the The Fourth Widening Natural Language Processing Workshop

The training objective of unidirectional language models (LMs) is similar to a psycholinguistic benchmark known as the cloze task, which measures next-word predictability. However, LMs lack the rich set of experiences that people do, and humans can be highly creative. To assess human parity in these models’ training objective, we compare the predictions of three neural language models to those of human participants in a freely available behavioral dataset (Luke & Christianson, 2016). Our results show that while neural models show a close correspondence to human productions, they nevertheless assign insufficient probability to how often speakers guess upcoming words, especially for open-class content words.

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Proceedings of the Second Workshop on Computational Research in Linguistic Typology
Ekaterina Vylomova | Edoardo M. Ponti | Eitan Grossman | Arya D. McCarthy | Yevgeni Berzak | Haim Dubossarsky | Ivan Vulić | Roi Reichart | Anna Korhonen | Ryan Cotterell
Proceedings of the Second Workshop on Computational Research in Linguistic Typology


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The SIGMORPHON 2019 Shared Task : Morphological Analysis in Context and Cross-Lingual Transfer for InflectionSIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection
Arya D. McCarthy | Ekaterina Vylomova | Shijie Wu | Chaitanya Malaviya | Lawrence Wolf-Sonkin | Garrett Nicolai | Christo Kirov | Miikka Silfverberg | Sabrina J. Mielke | Jeffrey Heinz | Ryan Cotterell | Mans Hulden
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages. The first task evolves past years’ inflection tasks by examining transfer of morphological inflection knowledge from a high-resource language to a low-resource language. This year also presents a new second challenge on lemmatization and morphological feature analysis in context. All submissions featured a neural component and built on either this year’s strong baselines or highly ranked systems from previous years’ shared tasks. Every participating team improved in accuracy over the baselines for the inflection task (though not Levenshtein distance), and every team in the contextual analysis task improved on both state-of-the-art neural and non-neural baselines.

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Proceedings of TyP-NLP: The First Workshop on Typology for Polyglot NLP
Haim Dubossarsky | Arya D. McCarthy | Edoardo Maria Ponti | Ivan Vulić | Ekaterina Vylomova | Yevgeni Berzak | Ryan Cotterell | Manaal Faruqui | Anna Korhonen | Roi Reichart
Proceedings of TyP-NLP: The First Workshop on Typology for Polyglot NLP

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Harnessing Indirect Training Data for End-to-End Automatic Speech Translation : Tricks of the Trade
Juan Pino | Liezl Puzon | Jiatao Gu | Xutai Ma | Arya D. McCarthy | Deepak Gopinath
Proceedings of the 16th International Conference on Spoken Language Translation

For automatic speech translation (AST), end-to-end approaches are outperformed by cascaded models that transcribe with automatic speech recognition (ASR), then trans- late with machine translation (MT). A major cause of the performance gap is that, while existing AST corpora are small, massive datasets exist for both the ASR and MT subsystems. In this work, we evaluate several data augmentation and pretraining approaches for AST, by comparing all on the same datasets. Simple data augmentation by translating ASR transcripts proves most effective on the EnglishFrench augmented LibriSpeech dataset, closing the performance gap from 8.2 to 1.4 BLEU, compared to a very strong cascade that could directly utilize copious ASR and MT data. The same end-to-end approach plus fine-tuning closes the gap on the EnglishRomanian MuST-C dataset from 6.7 to 3.7 BLEU. In addition to these results, we present practical rec- ommendations for augmentation and pretraining approaches. Finally, we decrease the performance gap to 0.01 BLEU us- ing a Transformer-based architecture.

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Weird Inflects but OK : Making Sense of Morphological Generation ErrorsOK: Making Sense of Morphological Generation Errors
Kyle Gorman | Arya D. McCarthy | Ryan Cotterell | Ekaterina Vylomova | Miikka Silfverberg | Magdalena Markowska
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

We conduct a manual error analysis of the CoNLL-SIGMORPHON Shared Task on Morphological Reinflection. This task involves natural language generation : systems are given a word in citation form (e.g., hug) and asked to produce the corresponding inflected form (e.g., the simple past hugged). We propose an error taxonomy and use it to annotate errors made by the top two systems across twelve languages. Many of the observed errors are related to inflectional patterns sensitive to inherent linguistic properties such as animacy or affect ; many others are failures to predict truly unpredictable inflectional behaviors. We also find nearly one quarter of the residual errors reflect errors in the gold data.


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Marrying Universal Dependencies and Universal MorphologyUniversal Dependencies and Universal Morphology
Arya D. McCarthy | Miikka Silfverberg | Ryan Cotterell | Mans Hulden | David Yarowsky
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languagesUD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project’s annotations could be used to validate the other’s. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13 % recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.

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Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
Brian Thompson | Huda Khayrallah | Antonios Anastasopoulos | Arya D. McCarthy | Kevin Duh | Rebecca Marvin | Paul McNamee | Jeremy Gwinnup | Tim Anderson | Philipp Koehn
Proceedings of the Third Conference on Machine Translation: Research Papers

To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component’s contribution to, and capacity for, domain adaptation. We find that freezing any single component during continued training has minimal impact on performance, and that performance is surprisingly good when a single component is adapted while holding the rest of the model fixed. We also find that continued training does not move the model very far from the out-of-domain model, compared to a sensitivity analysis metric, suggesting that the out-of-domain model can provide a good generic initialization for the new domain.