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Found 36 papers, 18 papers with code
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
Of particular interest, languages on the same branch of our phylogenetic tree are notably similar, whereas languages from separate branches are no more similar than chance.
Sometimes We Want Ungrammatical Translations
Rapid progress in Neural Machine Translation (NMT) systems over the last few years has focused primarily on improving translation quality, and as a secondary focus, improving robustness to perturbations (e. g. spelling).
Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network
Inflectional morphology has since long been a useful testing ground for broader questions about generalisation in language and the viability of neural network models as cognitive models of language.
"I'm sorry to hear that": finding bias in language models with a holistic descriptor dataset
As language models grow in popularity, their biases across all possible markers of demographic identity should be measured and addressed in order to avoid perpetuating existing societal harms.
Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality
We present a novel task and dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning, which we call Winoground.
Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks
We introduce Dynatask: an open source system for setting up custom NLP tasks that aims to greatly lower the technical knowledge and effort required for hosting and evaluating state-of-the-art NLP models, as well as for conducting model in the loop data collection with crowdworkers.
A Latent-Variable Model for Intrinsic Probing
The success of pre-trained contextualized representations has prompted researchers to analyze them for the presence of linguistic information.
A Word on Machine Ethics: A Response to Jiang et al. (2021)
Ethics is one of the longest standing intellectual endeavors of humanity.
Analyzing Dynamic Adversarial Training Data in the Limit
To create models that are robust across a wide range of test inputs, training datasets should include diverse examples that span numerous phenomena.
Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue models
All AI models are susceptible to learning biases in data that they are trained on.
Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking
We introduce Dynaboard, an evaluation-as-a-service framework for hosting benchmarks and conducting holistic model comparison, integrated with the Dynabench platform.
Investigating Failures of Automatic Translation in the Case of Unambiguous Gender
Transformer based models are the modern work horses for neural machine translation (NMT), reaching state of the art across several benchmarks.
Sometimes We Want Translationese
Rapid progress in Neural Machine Translation (NMT) systems over the last few years has been driven primarily towards improving translation quality, and as a secondary focus, improved robustness to input perturbations (e. g. spelling and grammatical mistakes).
Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little
A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent in classical NLP pipelines.
Dynabench: Rethinking Benchmarking in NLP
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking.
UnNatural Language Inference
We provide novel evidence that complicates this claim: we find that state-of-the-art Natural Language Inference (NLI) models assign the same labels to permuted examples as they do to the original, i. e. they are largely invariant to random word-order permutations.
To what extent do human explanations of model behavior align with actual model behavior?
Given the increasingly prominent role NLP models (will) play in our lives, it is important for human expectations of model behavior to align with actual model behavior.
ANLIzing the Adversarial Natural Language Inference Dataset
We perform an in-depth error analysis of Adversarial NLI (ANLI), a recently introduced large-scale human-and-model-in-the-loop natural language inference dataset collected over multiple rounds.
Intrinsic Probing through Dimension Selection
Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks.
Pareto Probing: Trading Off Accuracy for Complexity
In our contribution to this discussion, we argue for a probe metric that reflects the fundamental trade-off between probe complexity and performance: the Pareto hypervolume.
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
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 Tale of a Probe and a Parser
One such probe is the structural probe (Hewitt and Manning, 2019), designed to quantify the extent to which syntactic information is encoded in contextualised word representations.
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs
We also find that there are statistically significant relationships between the grammatical genders of inanimate nouns and the verbs that take those nouns as direct objects, as indirect objects, and as subjects.
Multi-Dimensional Gender Bias Classification
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
Predicting Declension Class from Form and Meaning
We find for two Indo-European languages (Czech and German) that form and meaning respectively share significant amounts of information with class (and contribute additional information above and beyond gender).
Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition
We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences.
Information-Theoretic Probing for Linguistic Structure
The success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually ``know'' about natural language.
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Models often easily learn biases present in the training data, and their predictions directly reflect this bias.
Adversarial NLI: A New Benchmark for Natural Language Understanding
We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure.
Quantifying the Semantic Core of Gender Systems
To that end, we use canonical correlation analysis to correlate the grammatical gender of inanimate nouns with an externally grounded definition of their lexical semantics.
On the Idiosyncrasies of the Mandarin Chinese Classifier System
While idiosyncrasies of the Chinese classifier system have been a richly studied topic among linguists (Adams and Conklin, 1973; Erbaugh, 1986; Lakoff, 1986), not much work has been done to quantify them with statistical methods.
Verb Argument Structure Alternations in Word and Sentence Embeddings
For converging evidence, we further construct LaVA, a corresponding word-level dataset, and investigate whether the same syntactic features can be extracted from word embeddings.
XNLI: Evaluating Cross-lingual Sentence Representations
State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models.
Ranked #5 on
Natural Language Inference
on XNLI French
Cross-Lingual Natural Language Inference
Machine Translation
+1
Do latent tree learning models identify meaningful structure in sentences?
Recent work on the problem of latent tree learning has made it possible to train neural networks that learn to both parse a sentence and use the resulting parse to interpret the sentence, all without exposure to ground-truth parse trees at training time.
The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017).
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding.
