106 citations · 347 across the 14 of their papers we have counts for
19 papers
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
UniMorph 4.0: Universal Morphology
Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa +93
The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world…
Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP
Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky +8
What are the units of text that we want to model? From bytes to multi-word expressions, text can be analyzed and generated at many granularities. Until recently, most natural langu…
SIGTYP 2021 Shared Task: Robust Spoken Language Identification
Elizabeth Salesky, Badr M. Abdullah, Sabrina J. Mielke +6
While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task. For many low-resource an…
Reducing conversational agents' overconfidence through linguistic calibration
Sabrina J. Mielke, Arthur Szlam, Emily Dinan +1
While improving neural dialogue agents' factual accuracy is the object of much research, another important aspect of communication, less studied in the setting of neural dialogue,…
SIGTYP 2020 Shared Task: Prediction of Typological Features
Johannes Bjerva, Elizabeth Salesky, Sabrina J. Mielke +6
Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages. They have been shown to be use…
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Ekaterina Vylomova, Jennifer White, Elizabeth Salesky +25
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…
Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset
Brian Roark, Lawrence Wolf-Sonkin, Christo Kirov +4
This paper describes the Dakshina dataset, a new resource consisting of text in both the Latin and native scripts for 12 South Asian languages. The dataset includes, for each langu…
It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information
Emanuele Bugliarello, Sabrina J. Mielke, Antonios Anastasopoulos +2
The performance of neural machine translation systems is commonly evaluated in terms of BLEU. However, due to its reliance on target language properties and generation, the BLEU me…
Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!
Suzanna Sia, Ayush Dalmia, Sabrina J. Mielke
Topic models are a useful analysis tool to uncover the underlying themes within document collections. The dominant approach is to use probabilistic topic models that posit a genera…
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection
Arya D. McCarthy, Ekaterina Vylomova, Shijie Wu +9
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 cont…
What Kind of Language Is Hard to Language-Model?
Sabrina J. Mielke, Ryan Cotterell, Kyle Gorman +2
How language-agnostic are current state-of-the-art NLP tools? Are there some types of language that are easier to model with current methods? In prior work (Cotterell et al., 2018)…
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach +1
Gender stereotypes are manifest in most of the world's languages and are consequently propagated or amplified by NLP systems. Although research has focused on mitigating gender ste…
UniMorph 2.0: Universal Morphology
Christo Kirov, Ryan Cotterell, John Sylak-Glassman +10
The Universal Morphology UniMorph project is a collaborative effort to improve how NLP handles complex morphology across the world's languages. The project releases annotated morph…
The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection
Ryan Cotterell, Christo Kirov, John Sylak-Glassman +10
The CoNLL--SIGMORPHON 2018 shared task on supervised learning of morphological generation featured data sets from 103 typologically diverse languages. Apart from extending the numb…
A Structured Variational Autoencoder for Contextual Morphological Inflection
Lawrence Wolf-Sonkin, Jason Naradowsky, Sabrina J. Mielke +1
Statistical morphological inflectors are typically trained on fully supervised, type-level data. One remaining open research question is the following: How can we effectively explo…
Are All Languages Equally Hard to Language-Model?
Ryan Cotterell, Sabrina J. Mielke, Jason Eisner +1
For general modeling methods applied to diverse languages, a natural question is: how well should we expect our models to work on languages with differing typological profiles? In…
Unsupervised Disambiguation of Syncretism in Inflected Lexicons
Ryan Cotterell, Christo Kirov, Sabrina J. Mielke +1
Lexical ambiguity makes it difficult to compute various useful statistics of a corpus. A given word form might represent any of several morphological feature bundles. One can, howe…
Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model
Sabrina J. Mielke, Jason Eisner
We show how the spellings of known words can help us deal with unknown words in open-vocabulary NLP tasks. The method we propose can be used to extend any closed-vocabulary generat…