Aryaman Arora


2024

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pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
Zhengxuan Wu | Atticus Geiger | Aryaman Arora | Jing Huang | Zheng Wang | Noah Goodman | Christopher Manning | Christopher Potts
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)

Interventions on model-internal states are fundamental operations in many areas of AI, including model editing, steering, robustness, and interpretability. To facilitate such research, we introduce pyvene, an open-source Python library that supports customizable interventions on a range of different PyTorch modules. pyvene supports complex intervention schemes with an intuitive configuration format, and its interventions can be static or include trainable parameters. We show how pyvene provides a unified and extensible framework for performing interventions on neural models and sharing the intervened upon models with others. We illustrate the power of the library via interpretability analyses using causal abstraction and knowledge localization. We publish our library through Python Package Index (PyPI) and provide code, documentation, and tutorials at ‘https://github.com/stanfordnlp/pyvene‘.

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Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokens
Nay San | Georgios Paraskevopoulos | Aryaman Arora | Xiluo He | Prabhjot Kaur | Oliver Adams | Dan Jurafsky
Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

While massively multilingual speech models like wav2vec 2.0 XLSR-128 can be directly fine-tuned for automatic speech recognition (ASR), downstream performance can still be relatively poor on languages that are under-represented in the pre-training data. Continued pre-training on 70–200 hours of untranscribed speech in these languages can help — but what about languages without that much recorded data? For such cases, we show that supplementing the target language with data from a similar, higher-resource ‘donor’ language can help. For example, continued pretraining on only 10 hours of low-resource Punjabi supplemented with 60 hours of donor Hindi is almost as good as continued pretraining on 70 hours of Punjabi. By contrast, sourcing supplemental data from less similar donors like Bengali does not improve ASR performance. To inform donor language selection, we propose a novel similarity metric based on the sequence distribution of induced acoustic units: the Acoustic Token Distribution Similarity (ATDS). Across a set of typologically different target languages (Punjabi, Galician, Iban, Setswana), we show that the ATDS between the target language and its candidate donors precisely predicts target language ASR performance.

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IruMozhi: Automatically classifying diglossia in Tamil
Kabilan Prasanna | Aryaman Arora
Findings of the Association for Computational Linguistics: NAACL 2024

Tamil, a Dravidian language of South Asia, is a highly diglossic language with two very different registers in everyday use: Literary Tamil (preferred in writing and formal communication) and Spoken Tamil (confined to speech and informal media). Spoken Tamil is under-studied in modern NLP systems compared to Literary Tamil written in the Tamil script, as evidenced by a lack of datasets explicitly targetting the Spoken variety. In this paper, we release IruMozhi, a human-translated dataset of parallel text in Literary and Spoken Tamil. Using IruMozhi, we train classifiers on the task of identifying which Tamil variety a text belongs to. We use these models to gauge the availability of pretraining data in Spoken Tamil, to audit the composition of existing labelled datasets for Tamil, and to encourage future work on the variety.

2023

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Jambu: A historical linguistic database for South Asian languages
Aryaman Arora | Adam Farris | Samopriya Basu | Suresh Kolichala
Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology

We introduce JAMBU, a cognate database of South Asian languages which unifies dozens of previous sources in a structured and accessible format. The database includes nearly 287k lemmata from 602 lects, grouped together in 23k sets of cognates. We outline the data wrangling necessary to compile the dataset and train neural models for reflex prediction on the Indo- Aryan subset of the data. We hope that JAMBU is an invaluable resource for all historical linguists and Indologists, and look towards further improvement and expansion of the database.

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SIGMORPHONUniMorph 2023 Shared Task 0: Typologically Diverse Morphological Inflection
Omer Goldman | Khuyagbaatar Batsuren | Salam Khalifa | Aryaman Arora | Garrett Nicolai | Reut Tsarfaty | Ekaterina Vylomova
Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology

The 2023 SIGMORPHON–UniMorph shared task on typologically diverse morphological inflection included a wide range of languages: 26 languages from 9 primary language families. The data this year was all lemma-split, to allow testing models’ generalization ability, and structured along the new hierarchical schema presented in (Batsuren et al., 2022). The systems submitted this year, 9 in number, showed ingenuity and innovativeness, including hard attention for explainability and bidirectional decoding. Special treatment was also given by many participants to the newly-introduced data in Japanese, due to the high abundance of unseen Kanji characters in its test set.

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Unified Syntactic Annotation of English in the CGEL Framework
Brett Reynolds | Aryaman Arora | Nathan Schneider
Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)

We investigate whether the Cambridge Grammar of the English Language (2002) and its extensive descriptions work well as a corpus annotation scheme. We develop annotation guidelines and in the process outline some interesting linguistic uncertainties that we had to resolve. To test the applicability of CGEL to real-world corpora, we conduct an interannotator study on sentences from the English Web Treebank, showing that consistent annotation of even complex syntactic phenomena like gapping using the CGEL formalism is feasible. Why introduce yet another formalism for English syntax? We argue that CGEL is attractive due to its exhaustive analysis of English syntactic phenomena, its labeling of both constituents and functions, and its accessibility. We look towards expanding CGELBank and augmenting it with automatic conversions from existing treebanks in the future.

2022

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Computational Historical Linguistics and Language Diversity in South Asia
Aryaman Arora | Adam Farris | Samopriya Basu | Suresh Kolichala
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

South Asia is home to a plethora of languages, many of which severely lack access to new language technologies. This linguistic diversity also results in a research environment conducive to the study of comparative, contact, and historical linguistics–fields which necessitate the gathering of extensive data from many languages. We claim that data scatteredness (rather than scarcity) is the primary obstacle in the development of South Asian language technology, and suggest that the study of language history is uniquely aligned with surmounting this obstacle. We review recent developments in and at the intersection of South Asian NLP and historical-comparative linguistics, describing our and others’ current efforts in this area. We also offer new strategies towards breaking the data barrier.

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Estimating the Entropy of Linguistic Distributions
Aryaman Arora | Clara Meister | Ryan Cotterell
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Shannon entropy is often a quantity of interest to linguists studying the communicative capacity of human language. However, entropymust typically be estimated from observed data because researchers do not have access to the underlying probability distribution. While entropy estimation is a well-studied problem in other fields, there is not yet a comprehensive exploration of the efficacy of entropy estimators for use with linguistic data. In this work, we fill this void, studying the empirical effectiveness of different entropy estimators for linguistic distributions. In a replication of two recent information-theoretic linguistic studies, we find evidence that the reported effect size is over-estimated due to over-reliance on poor entropy estimators. We end this paper with a concrete recommendation for the entropy estimators that should be used in future linguistic studies.

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UniMorph 4.0: Universal Morphology
Khuyagbaatar Batsuren | Omer Goldman | Salam Khalifa | Nizar Habash | Witold Kieraś | Gábor Bella | Brian Leonard | Garrett Nicolai | Kyle Gorman | Yustinus Ghanggo Ate | Maria Ryskina | Sabrina Mielke | Elena Budianskaya | Charbel El-Khaissi | Tiago Pimentel | Michael Gasser | William Abbott Lane | Mohit Raj | Matt Coler | Jaime Rafael Montoya Samame | Delio Siticonatzi Camaiteri | Esaú Zumaeta Rojas | Didier López Francis | Arturo Oncevay | Juan López Bautista | Gema Celeste Silva Villegas | Lucas Torroba Hennigen | Adam Ek | David Guriel | Peter Dirix | Jean-Philippe Bernardy | Andrey Scherbakov | Aziyana Bayyr-ool | Antonios Anastasopoulos | Roberto Zariquiey | Karina Sheifer | Sofya Ganieva | Hilaria Cruz | Ritván Karahóǧa | Stella Markantonatou | George Pavlidis | Matvey Plugaryov | Elena Klyachko | Ali Salehi | Candy Angulo | Jatayu Baxi | Andrew Krizhanovsky | Natalia Krizhanovskaya | Elizabeth Salesky | Clara Vania | Sardana Ivanova | Jennifer White | Rowan Hall Maudslay | Josef Valvoda | Ran Zmigrod | Paula Czarnowska | Irene Nikkarinen | Aelita Salchak | Brijesh Bhatt | Christopher Straughn | Zoey Liu | Jonathan North Washington | Yuval Pinter | Duygu Ataman | Marcin Wolinski | Totok Suhardijanto | Anna Yablonskaya | Niklas Stoehr | Hossep Dolatian | Zahroh Nuriah | Shyam Ratan | Francis M. Tyers | Edoardo M. Ponti | Grant Aiton | Aryaman Arora | Richard J. Hatcher | Ritesh Kumar | Jeremiah Young | Daria Rodionova | Anastasia Yemelina | Taras Andrushko | Igor Marchenko | Polina Mashkovtseva | Alexandra Serova | Emily Prud’hommeaux | Maria Nepomniashchaya | Fausto Giunchiglia | Eleanor Chodroff | Mans Hulden | Miikka Silfverberg | Arya D. McCarthy | David Yarowsky | Ryan Cotterell | Reut Tsarfaty | Ekaterina Vylomova
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.

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MASALA: Modelling and Analysing the Semantics of Adpositions in Linguistic Annotation of Hindi
Aryaman Arora | Nitin Venkateswaran | Nathan Schneider
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We present a completed, publicly available corpus of annotated semantic relations of adpositions and case markers in Hindi. We used the multilingual SNACS annotation scheme, which has been applied to a variety of typologically diverse languages. Building on past work examining linguistic problems in SNACS annotation, we use language models to attempt automatic labelling of SNACS supersenses in Hindi and achieve results competitive with past work on English. We look towards upstream applications in semantic role labelling and extension to related languages such as Gujarati.

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Universal Dependencies for Punjabi
Aryaman Arora
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We introduce the first Universal Dependencies treebank for Punjabi (written in the Gurmukhi script) and discuss corpus design and linguistic phenomena encountered in annotation. The treebank covers a variety of genres and has been annotated for POS tags, dependency relations, and graph-based Enhanced Dependencies. We aim to expand the diversity of coverage of Indo-Aryan languages in UD.

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The SIGMORPHON 2022 Shared Task on Morpheme Segmentation
Khuyagbaatar Batsuren | Gábor Bella | Aryaman Arora | Viktor Martinovic | Kyle Gorman | Zdeněk Žabokrtský | Amarsanaa Ganbold | Šárka Dohnalová | Magda Ševčíková | Kateřina Pelegrinová | Fausto Giunchiglia | Ryan Cotterell | Ekaterina Vylomova
Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections. Subtask 1, word-level morpheme segmentation, covered 5 million words in 9 languages (Czech, English, Spanish, Hungarian, French, Italian, Russian, Latin, Mongolian) and received 13 system submissions from 7 teams and the best system averaged 97.29% F1 score across all languages, ranging English (93.84%) to Latin (99.38%). Subtask 2, sentence-level morpheme segmentation, covered 18,735 sentences in 3 languages (Czech, English, Mongolian), received 10 system submissions from 3 teams, and the best systems outperformed all three state-of-the-art subword tokenization methods (BPE, ULM, Morfessor2) by 30.71% absolute. To facilitate error analysis and support any type of future studies, we released all system predictions, the evaluation script, and all gold standard datasets.

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SIGMORPHONUniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection
Jordan Kodner | Salam Khalifa | Khuyagbaatar Batsuren | Hossep Dolatian | Ryan Cotterell | Faruk Akkus | Antonios Anastasopoulos | Taras Andrushko | Aryaman Arora | Nona Atanalov | Gábor Bella | Elena Budianskaya | Yustinus Ghanggo Ate | Omer Goldman | David Guriel | Simon Guriel | Silvia Guriel-Agiashvili | Witold Kieraś | Andrew Krizhanovsky | Natalia Krizhanovsky | Igor Marchenko | Magdalena Markowska | Polina Mashkovtseva | Maria Nepomniashchaya | Daria Rodionova | Karina Scheifer | Alexandra Sorova | Anastasia Yemelina | Jeremiah Young | Ekaterina Vylomova
Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

The 2022 SIGMORPHON–UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe. We emphasize generalization along different dimensions this year by evaluating test items with unseen lemmas and unseen features separately under small and large training conditions. Across the five submitted systems and two baselines, the prediction of inflections with unseen features proved challenging, with average performance decreased substantially from last year. This was true even for languages for which the forms were in principle predictable, which suggests that further work is needed in designing systems that capture the various types of generalization required for the world’s languages.

2021

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For the Purpose of Curry: A UD Treebank for Ashokan Prakrit
Adam Farris | Aryaman Arora
Proceedings of the Fifth Workshop on Universal Dependencies (UDW, SyntaxFest 2021)

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Bhāṣācitra: Visualising the dialect geography of South Asia
Aryaman Arora | Adam Farris | Gopalakrishnan R | Samopriya Basu
Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021

We present Bhāṣācitra, a dialect mapping system for South Asia built on a database of linguistic studies of languages of the region annotated for topic and location data. We analyse language coverage and look towards applications to typology by visualising example datasets. The application is not only meant to be useful for feature mapping, but also serves as a new kind of interactive bibliography for linguists of South Asian languages.

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SNACS Annotation of Case Markers and Adpositions in Hindi
Aryaman Arora | Nitin Venkateswaran | Nathan Schneider
Proceedings of the Society for Computation in Linguistics 2021

2020

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Supervised Grapheme-to-Phoneme Conversion of Orthographic Schwas in Hindi and Punjabi
Aryaman Arora | Luke Gessler | Nathan Schneider
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Hindi grapheme-to-phoneme (G2P) conversion is mostly trivial, with one exception: whether a schwa represented in the orthography is pronounced or unpronounced (deleted). Previous work has attempted to predict schwa deletion in a rule-based fashion using prosodic or phonetic analysis. We present the first statistical schwa deletion classifier for Hindi, which relies solely on the orthography as the input and outperforms previous approaches. We trained our model on a newly-compiled pronunciation lexicon extracted from various online dictionaries. Our best Hindi model achieves state of the art performance, and also achieves good performance on a closely related language, Punjabi, without modification.

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PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English
Michael Kranzlein | Emma Manning | Siyao Peng | Shira Wein | Aryaman Arora | Nathan Schneider
Proceedings of the 14th Linguistic Annotation Workshop

We present the Prepositions Annotated with Supsersense Tags in Reddit International English (“PASTRIE”) corpus, a new dataset containing manually annotated preposition supersenses of English data from presumed speakers of four L1s: English, French, German, and Spanish. The annotations are comprehensive, covering all preposition types and tokens in the sample. Along with the corpus, we provide analysis of distributional patterns across the included L1s and a discussion of the influence of L1s on L2 preposition choice.
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