Search Results for author:
Found 17 papers, 7 papers with code
SIGMORPHON–UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection
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.
Well-Defined Morphology is Sentence-Level Morphology
Morphological tasks have gained decent popularity within the NLP community in the recent years, with large multi-lingual datasets providing morphological analysis of words, either in or out of context.
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance
In the domain of Morphology, Inflection is a fundamental and important task that gained a lot of traction in recent years, mostly via SIGMORPHON’s shared-tasks. With average accuracy above 0. 9 over the scores of all languages, the task is considered mostly solved using relatively generic neural seq2seq models, even with little data provided. In this work, we propose to re-evaluate morphological inflection models by employing harder train-test splits that will challenge the generalization capacity of the models.
SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages
This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features.
Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance
Despite it being the cornerstone of BPE, the most common tokenization algorithm, the importance of compression in the tokenization process is still unclear.
Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew
We investigate the hypothesis that incorporating explicit morphological knowledge in the pre-training phase can improve the performance of PLMs for MRLs.
Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces
The ability to identify and control different kinds of linguistic information encoded in vector representations of words has many use cases, especially for explainability and bias removal.
The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models
In this paper, we explore the behavior of LLMs when presented with (un)answerable queries.
Morphological Inflection with Phonological Features
Recent years have brought great advances into solving morphological tasks, mostly due to powerful neural models applied to various tasks as (re)inflection and analysis.
Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks
Data contamination has become prevalent and challenging with the rise of models pretrained on large automatically-crawled corpora.
UniMorph 4.0: Universal Morphology
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.
Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study
In recent years, a flurry of morphological datasets had emerged, most notably UniMorph, a multi-lingual repository of inflection tables.
Morphology Without Borders: Clause-Level Morphology
We use this dataset to derive 3 clause-level morphological tasks: inflection, reinflection and analysis.
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance
The effect is most significant for low-resourced languages with a drop as high as 95 points, but even high-resourced languages lose about 10 points on average.
Minimal Supervision for Morphological Inflection
Neural models for the various flavours of morphological inflection tasks have proven to be extremely accurate given ample labeled data -- data that may be slow and costly to obtain.
Weakly Supervised Semantic Parsing with Abstract Examples
Training semantic parsers from weak supervision (denotations) rather than strong supervision (programs) complicates training in two ways.
Weakly-supervised Semantic Parsing with Abstract Examples
Training semantic parsers from weak supervision (denotations) rather than strong supervision (programs) complicates training in two ways.
