* denotes equal contribution

dissertation

1. PhD

   Learning Computational Models of Non-Standard Language

   Maria Ryskina

   Carnegie Mellon University, 2022

   Abs PDF

   Non-standard language such as novel words or creative spellings of existing ones often occurs in natural text corpora, posing significant challenges for natural language processing (NLP) models. While humans can successfully infer the meaning communicated in such non-standard ways, NLP models largely discard linguistic innovation as noise, ignoring its fundamentally non-random nature and losing valuable context. In this thesis, we focus on computational modeling of such creative phenomena, aiming to both improve the automatic processing of non-standardized text data and to learn more about the linguistic and cognitive factors that allow humans to produce and understand novel linguistic items. We present empirical studies of several phenomena under the umbrella of non-standard language, characterized in terms of different linguistic units (orthographic, morphological, or lexical) and considered at different levels of granularity (from individual users to entire dialects or languages). First, we show how idiosyncratic spelling preferences reveal information about the user, with an application to the bibliographic task of identifying typesetters of historical printed documents. Second, we discuss the common patterns in user-specific orthographies and demonstrate that incorporating these patterns helps with unsupervised conversion of idiosyncratically romanized text into the language’s native orthography. Third, we consider word emergence in a dialect or language as a whole and, in two diachronic corpora studies, model the language-internal and language-external factors that drive it. Finally, we look at how continuous emergence of novel words is reconciled with the existing system of morphological rules, focusing on generalization to unseen lemmas in morphological inflection in several languages.

journal articles

2023

1. Nat Mach Intell

   A taxonomy and review of generalization research in NLP

   Dieuwke Hupkes, Mario Giulianelli, Verna Dankers, and 17 more authors

   Nature Machine Intelligence, 2023

   Abs arXiv PDF Website

   The ability to generalize well is one of the primary desiderata for models of natural language processing (NLP), but what ‘good generalization’ entails and how it should be evaluated is not well understood. In this Analysis we present a taxonomy for characterizing and understanding generalization research in NLP. The proposed taxonomy is based on an extensive literature review and contains five axes along which generalization studies can differ: their main motivation, the type of generalization they aim to solve, the type of data shift they consider, the source by which this data shift originated, and the locus of the shift within the NLP modelling pipeline. We use our taxonomy to classify over 700 experiments, and we use the results to present an in-depth analysis that maps out the current state of generalization research in NLP and make recommendations for which areas deserve attention in the future.

conference & workshop papers

2023

1. FAccTBest Paper Award

   Queer In AI: A Case Study in Community-Led Participatory AI

   Organizers of Queer in AI, and 50 more authors

   In ACM Conference on Fairness, Accountability, and Transparency, 2023

   Abs arXiv

   We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community’s programs over the years. We discuss different challenges that emerged in the process, look at ways this organization has fallen short of operationalizing participatory and intersectional principles, and then assess the organization’s impact. Queer in AI provides important lessons and insights for practitioners and theorists of participatory methods broadly through its rejection of hierarchy in favor of decentralization, success at building aid and programs by and for the queer community, and effort to change actors and institutions outside of the queer community. Finally, we theorize how communities like Queer in AI contribute to the participatory design in AI more broadly by fostering cultures of participation in AI, welcoming and empowering marginalized participants, critiquing poor or exploitative participatory practices, and bringing participation to institutions outside of individual research projects. Queer in AI’s work serves as a case study of grassroots activism and participatory methods within AI, demonstrating the potential of community-led participatory methods and intersectional praxis, while also providing challenges, case studies, and nuanced insights to researchers developing and using participatory methods.

2022

1. LREC

   UniMorph 4.0: Universal Morphology

   Khuyagbaatar Batsuren*, Omer Goldman*, and 93 more authors

   In Proc. Language Resources and Evaluation Conference, 2022

   Abs PDF Data

   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.

2021

1. BlackboxNLP

   Learning Mathematical Properties of Integers

   Maria Ryskina, and Kevin Knight

   In Proc. BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2021

   Abs PDF Data Poster

   Embedding words in high-dimensional vector spaces has proven valuable in many natural language applications. In this work, we investigate whether similarly-trained embeddings of integers can capture concepts that are useful for mathematical applications. We probe the integer embeddings for mathematical knowledge, apply them to a set of numerical reasoning tasks, and show that by learning the representations from mathematical sequence data, we can substantially improve over number embeddings learned from English text corpora.

2. SIGMORPHON

   SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages

   Tiago Pimentel*, Maria Ryskina*, and 56 more authors

   In Proc. SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, 2021

   Abs PDF Code

   This year’s iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features. In terms of the task, we enrich UniMorph with new data for 32 languages from 13 language families, with most of them being under-resourced: Kunwinjku, Classical Syriac, Arabic (Modern Standard, Egyptian, Gulf), Hebrew, Amharic, Aymara, Magahi, Braj, Kurdish (Central, Northern, Southern), Polish, Karelian, Livvi, Ludic, Veps, Võro, Evenki, Xibe, Tuvan, Sakha, Turkish, Indonesian, Kodi, Seneca, Asháninka, Yanesha, Chukchi, Itelmen, Eibela. We evaluate six systems on the new data and conduct an extensive error analysis of the systems’ predictions. Transformer-based models generally demonstrate superior performance on the majority of languages, achieving \textgreater90% accuracy on 65% of them. The languages on which systems yielded low accuracy are mainly under-resourced, with a limited amount of data. Most errors made by the systems are due to allomorphy, honorificity, and form variation. In addition, we observe that systems especially struggle to inflect multiword lemmas. The systems also produce misspelled forms or end up in repetitive loops (e.g., RNN-based models). Finally, we report a large drop in systems’ performance on previously unseen lemmas.

3. SIGMORPHON

   Comparative Error Analysis in Neural and Finite-state Models for Unsupervised Character-level Transduction

   Maria Ryskina, Eduard Hovy, Taylor Berg-Kirkpatrick, and Matthew R. Gormley

   In Proc. SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, 2021

   Abs PDF Slides Talk

   Traditionally, character-level transduction problems have been solved with finite-state models designed to encode structural and linguistic knowledge of the underlying process, whereas recent approaches rely on the power and flexibility of sequence-to-sequence models with attention. Focusing on the less explored unsupervised learning scenario, we compare the two model classes side by side and find that they tend to make different types of errors even when achieving comparable performance. We analyze the distributions of different error classes using two unsupervised tasks as testbeds: converting informally romanized text into the native script of its language (for Russian, Arabic, and Kannada) and translating between a pair of closely related languages (Serbian and Bosnian). Finally, we investigate how combining finite-state and sequence-to-sequence models at decoding time affects the output quantitatively and qualitatively.

4. EACL

   NoiseQA: Challenge Set Evaluation for User-Centric Question Answering

   Abhilasha Ravichander, Siddharth Dalmia, Maria Ryskina, Florian Metze, Eduard Hovy, and Alan W Black

   In Proc. European Chapter of the Association for Computational Linguistics, 2021

   Abs PDF Website

   When Question-Answering (QA) systems are deployed in the real world, users query them through a variety of interfaces, such as speaking to voice assistants, typing questions into a search engine, or even translating questions to languages supported by the QA system. While there has been significant community attention devoted to identifying correct answers in passages assuming a perfectly formed question, we show that components in the pipeline that precede an answering engine can introduce varied and considerable sources of error, and performance can degrade substantially based on these upstream noise sources even for powerful pre-trained QA models. We conclude that there is substantial room for progress before QA systems can be effectively deployed, highlight the need for QA evaluation to expand to consider real-world use, and hope that our findings will spur greater community interest in the issues that arise when our systems actually need to be of utility to humans.

2020

1. ACL

   Phonetic and Visual Priors for Decipherment of Informal Romanization

   Maria Ryskina, Matthew R. Gormley, and Taylor Berg-Kirkpatrick

   In Proc. Association for Computational Linguistics, 2020

   Abs PDF Code Slides Talk

   Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script languages into Latin character sets found on common keyboards. Character substitution choices differ between users but have been shown to be governed by the same main principles observed across a variety of languages—namely, character pairs are often associated through phonetic or visual similarity. We propose a noisy-channel WFST cascade model for deciphering the original non-Latin script from observed romanized text in an unsupervised fashion. We train our model directly on romanized data from two languages: Egyptian Arabic and Russian. We demonstrate that adding inductive bias through phonetic and visual priors on character mappings substantially improves the model’s performance on both languages, yielding results much closer to the supervised skyline. Finally, we introduce a new dataset of romanized Russian, collected from a Russian social network website and partially annotated for our experiments.

2. SCiL

   Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods

   Maria Ryskina, Ella Rabinovich, Taylor Berg-Kirkpatrick, David R. Mortensen, and Yulia Tsvetkov

   In Proc. Society for Computation in Linguistics, 2020

   Abs PDF Code Poster

   We perform statistical analysis of the phenomenon of neology, the process by which new words emerge in a language, using large diachronic corpora of English. We investigate the importance of two factors, semantic sparsity and frequency growth rates of semantic neighbors, formalized in the distributional semantics paradigm. We show that both factors are predictive of word emergence although we find more support for the latter hypothesis. Besides presenting a new linguistic application of distributional semantics, this study tackles the linguistic question of the role of language-internal factors (in our case, sparsity) in language change motivated by language-external factors (reflected in frequency growth).

2019

1. TAC

   OPERA: Operations-oriented Probabilistic Extraction, Reasoning, and Analysis

   Eduard Hovy, Jaime Carbonell, and 17 more authors

   In Proc. Text Analysis Conference, 2019

   Abs PDF

   The OPERA system of CMU and USC/ISI performs end-to-end information extraction from multiple media and languages (English, Russian, Ukrainian), integrates the results, builds Knowledge Bases about the domain, and does hypothesis creation and reasoning to answer questions.

2017

1. ACL

   Automatic Compositor Attribution in the First Folio of Shakespeare

   Maria Ryskina, Hannah Alpert-Abrams, Dan Garrette, and Taylor Berg-Kirkpatrick

   In Proc. Association for Computational Linguistics, 2017

   Abs PDF Poster

   Compositor attribution, the clustering of pages in a historical printed document by the individual who set the type, is a bibliographic task that relies on analysis of orthographic variation and inspection of visual details of the printed page. In this paper, we introduce a novel unsupervised model that jointly describes the textual and visual features needed to distinguish compositors. Applied to images of Shakespeare’s First Folio, our model predicts attributions that agree with the manual judgements of bibliographers with an accuracy of 87%, even on text that is the output of OCR.

preprints & other

2022

1. XRDS

   Queer in AI

   Hetvi Jethwani, Arjun Subramonian, William Agnew, and 4 more authors

   XRDS: Crossroads, The ACM Magazine for Students, 2022

   Abs PDF

   Queer in AI is an organization that aims to combat the harms faced by queer researchers within AI. Several inclusion initiatives are outlined, including those centered on policy and financial aid.

2021

1. arXiv

   Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play

   Arkady Arkhangorodsky, Scot Fang, Victoria Knight, Ajay Nagesh, Maria Ryskina, and Kevin Knight

   arXiv preprint, 2021

   Abs arXiv

   Task-oriented dialog systems are often trained on human/human dialogs, such as collected from Wizard-of-Oz interfaces. However, human/human corpora are frequently too small for supervised training to be effective. This paper investigates two approaches to training agent-bots and user-bots through self-play, in which they autonomously explore an API environment, discovering communication strategies that enable them to solve the task. We give empirical results for both reinforcement learning and game-theoretic equilibrium finding.