Lucas Torroba Hennigen

Also published as: Lucas Torroba Hennigen


2022

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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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Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models
Karolina Stanczak | Edoardo Ponti | Lucas Torroba Hennigen | Ryan Cotterell | Isabelle Augenstein
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The success of multilingual pre-trained models is underpinned by their ability to learn representations shared by multiple languages even in absence of any explicit supervision. However, it remains unclear how these models learn to generalise across languages. In this work, we conjecture that multilingual pre-trained models can derive language-universal abstractions about grammar. In particular, we investigate whether morphosyntactic information is encoded in the same subset of neurons in different languages. We conduct the first large-scale empirical study over 43 languages and 14 morphosyntactic categories with a state-of-the-art neuron-level probe. Our findings show that the cross-lingual overlap between neurons is significant, but its extent may vary across categories and depends on language proximity and pre-training data size.

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Probing as Quantifying Inductive Bias
Alexander Immer | Lucas Torroba Hennigen | Vincent Fortuin | Ryan Cotterell
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Pre-trained contextual representations have led to dramatic performance improvements on a range of downstream tasks. Such performance improvements have motivated researchers to quantify and understand the linguistic information encoded in these representations. In general, researchers quantify the amount of linguistic information through probing, an endeavor which consists of training a supervised model to predict a linguistic property directly from the contextual representations. Unfortunately, this definition of probing has been subject to extensive criticism in the literature, and has been observed to lead to paradoxical and counter-intuitive results. In the theoretical portion of this paper, we take the position that the goal of probing ought to be measuring the amount of inductive bias that the representations encode on a specific task. We further describe a Bayesian framework that operationalizes this goal and allows us to quantify the representations’ inductive bias. In the empirical portion of the paper, we apply our framework to a variety of NLP tasks. Our results suggest that our proposed framework alleviates many previous problems found in probing. Moreover, we are able to offer concrete evidence that—for some tasks—fastText can offer a better inductive bias than BERT.

2021

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Classifying Dyads for Militarized Conflict Analysis
Niklas Stoehr | Lucas Torroba Hennigen | Samin Ahbab | Robert West | Ryan Cotterell
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Understanding the origins of militarized conflict is a complex, yet important undertaking. Existing research seeks to build this understanding by considering bi-lateral relationships between entity pairs (dyadic causes) and multi-lateral relationships among multiple entities (systemic causes). The aim of this work is to compare these two causes in terms of how they correlate with conflict between two entities. We do this by devising a set of textual and graph-based features which represent each of the causes. The features are extracted from Wikipedia and modeled as a large graph. Nodes in this graph represent entities connected by labeled edges representing ally or enemy-relationships. This allows casting the problem as an edge classification task, which we term dyad classification. We propose and evaluate classifiers to determine if a particular pair of entities are allies or enemies. Our results suggest that our systemic features might be slightly better correlates of conflict. Further, we find that Wikipedia articles of allies are semantically more similar than enemies.

2020

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Machine Reading of Historical Events
Or Honovich | Lucas Torroba Hennigen | Omri Abend | Shay B. Cohen
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities. Within this broad framework, we address the task of machine reading the time of historical events, compile datasets for the task, and develop a model for tackling it. Given a brief textual description of an event, we show that good performance can be achieved by extracting relevant sentences from Wikipedia, and applying a combination of task-specific and general-purpose feature embeddings for the classification. Furthermore, we establish a link between the historical event ordering task and the event focus time task from the information retrieval literature, showing they also provide a challenging test case for machine reading algorithms.

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Intrinsic Probing through Dimension Selection
Lucas Torroba Hennigen | Adina Williams | Ryan Cotterell
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks. Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it. In this paper, we draw a distinction between intrinsic probing, which examines how linguistic information is structured within a representation, and the extrinsic probing popular in prior work, which only argues for the presence of such information by showing that it can be successfully extracted. To enable intrinsic probing, we propose a novel framework based on a decomposable multivariate Gaussian probe that allows us to determine whether the linguistic information in word embeddings is dispersed or focal. We then probe fastText and BERT for various morphosyntactic attributes across 36 languages. We find that most attributes are reliably encoded by only a few neurons, with fastText concentrating its linguistic structure more than BERT.

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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Ekaterina Vylomova | Jennifer White | Elizabeth Salesky | Sabrina J. Mielke | Shijie Wu | Edoardo Maria Ponti | Rowan Hall Maudslay | Ran Zmigrod | Josef Valvoda | Svetlana Toldova | Francis Tyers | Elena Klyachko | Ilya Yegorov | Natalia Krizhanovsky | Paula Czarnowska | Irene Nikkarinen | Andrew Krizhanovsky | Tiago Pimentel | Lucas Torroba Hennigen | Christo Kirov | Garrett Nicolai | Adina Williams | Antonios Anastasopoulos | Hilaria Cruz | Eleanor Chodroff | Ryan Cotterell | Miikka Silfverberg | Mans Hulden
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems’ ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed competitive and even superior performance on some languages (such as Ingrian, Tajik, Tagalog, Zarma, Lingala), especially with very limited data. Some language families (Afro-Asiatic, Niger-Congo, Turkic) were relatively easy for most systems and achieved over 90% mean accuracy while others were more challenging.
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