Cassandra L. Jacobs

Also published as: Cassandra Jacobs


2020

pdf bib
UniMorph 3.0: Universal Morphology
Arya D. McCarthy | Christo Kirov | Matteo Grella | Amrit Nidhi | Patrick Xia | Kyle Gorman | Ekaterina Vylomova | Sabrina J. Mielke | Garrett Nicolai | Miikka Silfverberg | Timofey Arkhangelskiy | Nataly Krizhanovsky | Andrew Krizhanovsky | Elena Klyachko | Alexey Sorokin | John Mansfield | Valts Ernštreits | Yuval Pinter | Cassandra L. Jacobs | Ryan Cotterell | Mans Hulden | David Yarowsky
Proceedings of the 12th Language Resources and Evaluation Conference

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological paradigms 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. We have implemented several improvements to the extraction pipeline which creates most of our data, so that it is both more complete and more correct. We have added 66 new languages, as well as new parts of speech for 12 languages. We have also amended the schema in several ways. Finally, we present three new community tools: two to validate data for resource creators, and one to make morphological data available from the command line. UniMorph is based at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University in Baltimore, Maryland. This paper details advances made to the schema, tooling, and dissemination of project resources since the UniMorph 2.0 release described at LREC 2018.

pdf bib
Will it Unblend?
Yuval Pinter | Cassandra L. Jacobs | Jacob Eisenstein
Findings of the Association for Computational Linguistics: EMNLP 2020

Natural language processing systems often struggle with out-of-vocabulary (OOV) terms, which do not appear in training data. Blends, such as “innoventor”, are one particularly challenging class of OOV, as they are formed by fusing together two or more bases that relate to the intended meaning in unpredictable manners and degrees. In this work, we run experiments on a novel dataset of English OOV blends to quantify the difficulty of interpreting the meanings of blends by large-scale contextual language models such as BERT. We first show that BERT’s processing of these blends does not fully access the component meanings, leaving their contextual representations semantically impoverished. We find this is mostly due to the loss of characters resulting from blend formation. Then, we assess how easily different models can recognize the structure and recover the origin of blends, and find that context-aware embedding systems outperform character-level and context-free embeddings, although their results are still far from satisfactory.

pdf bib
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Emmanuele Chersoni | Cassandra Jacobs | Yohei Oseki | Laurent Prévot | Enrico Santus
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

pdf bib
NYTWIT: A Dataset of Novel Words in the New York Times
Yuval Pinter | Cassandra L. Jacobs | Max Bittker
Proceedings of the 28th International Conference on Computational Linguistics

We present the New York Times Word Innovation Types dataset, or NYTWIT, a collection of over 2,500 novel English words published in the New York Times between November 2017 and March 2019, manually annotated for their class of novelty (such as lexical derivation, dialectal variation, blending, or compounding). We present baseline results for both uncontextual and contextual prediction of novelty class, showing that there is room for improvement even for state-of-the-art NLP systems. We hope this resource will prove useful for linguists and NLP practitioners by providing a real-world environment of novel word appearance.

bib
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.

2019

pdf bib
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Emmanuele Chersoni | Cassandra Jacobs | Alessandro Lenci | Tal Linzen | Laurent Prévot | Enrico Santus
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

pdf bib
Encoder-decoder models for latent phonological representations of words
Cassandra L. Jacobs | Fred Mailhot
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

We use sequence-to-sequence networks trained on sequential phonetic encoding tasks to construct compositional phonological representations of words. We show that the output of an encoder network can predict the phonetic durations of American English words better than a number of alternative forms. We also show that the model’s learned representations map onto existing measures of words’ phonological structure (phonological neighborhood density and phonotactic probability).

2018

pdf bib
Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018)
Asad Sayeed | Cassandra Jacobs | Tal Linzen | Marten van Schijndel
Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018)

2015

pdf bib
Predictions for self-priming from incremental updating models unifying comprehension and production
Cassandra L. Jacobs
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics