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Found 22 papers, 7 papers with code
Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection
Even though it is such a fundamental tool in NLP, crowdsourcing use is largely guided by common practices and the personal experience of researchers.
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.
WebIE: Faithful and Robust Information Extraction on the Web
We evaluate the in-domain, out-of-domain, and zero-shot cross-lingual performance of generative IE models and find models trained on WebIE show better generalisability.
Endowing Language Models with Multimodal Knowledge Graph Representations
We use the recently released VisualSem KG as our external knowledge repository, which covers a subset of Wikipedia and WordNet entities, and compare a mix of tuple-based and graph-based algorithms to learn entity and relation representations that are grounded on the KG multimodal information.
Multilingual Named Entity Recognition
named-entity-recognition
+2
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.
IndoNLI: A Natural Language Inference Dataset for Indonesian
The expert-annotated data is used exclusively as a test set.
Comparing Test Sets with Item Response Theory
Recent years have seen numerous NLP datasets introduced to evaluate the performance of fine-tuned models on natural language understanding tasks.
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?
However, we find that training crowdworkers, and then using an iterative process of collecting data, sending feedback, and qualifying workers based on expert judgments is an effective means of collecting challenging data.
Asking Crowdworkers to Write Entailment Examples: The Best of Bad Options
Using these protocols and a writing-based baseline, we collect several new English NLI datasets of over 3k examples each, each using a fixed amount of annotator time, but a varying number of examples to fit that time budget.
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models
To measure some forms of social bias in language models against protected demographic groups in the US, we introduce the Crowdsourced Stereotype Pairs benchmark (CrowS-Pairs).
VisualSem: A High-quality Knowledge Graph for Vision and Language
We also release a neural multi-modal retrieval model that can use images or sentences as inputs and retrieves entities in the KG.
Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?
However, we fail to observe more granular correlations between probing and target task performance, highlighting the need for further work on broad-coverage probing benchmarks.
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too
Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings.
Ranked #20 on
Zero-Shot Cross-Lingual Transfer
on XTREME
Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?
However, we fail to observe more granular correlations between probing and target task performance, highlighting the need for further work on broad-coverage probing benchmarks.
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages
Parsers are available for only a handful of the world's languages, since they require lots of training data.
LINSPECTOR: Multilingual Probing Tasks for Word Representations
We present a reusable methodology for creation and evaluation of such tests in a multilingual setting.
Explicitly modeling case improves neural dependency parsing
Neural dependency parsing models that compose word representations from characters can presumably exploit morphosyntax when making attachment decisions.
What do character-level models learn about morphology? The case of dependency parsing
When parsing morphologically-rich languages with neural models, it is beneficial to model input at the character level, and it has been claimed that this is because character-level models learn morphology.
UParse: the Edinburgh system for the CoNLL 2017 UD shared task
This paper presents our submissions for the CoNLL 2017 UD Shared Task.
From Characters to Words to in Between: Do We Capture Morphology?
Words can be represented by composing the representations of subword units such as word segments, characters, and/or character n-grams.
