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Found 49 papers, 28 papers with code
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).
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.
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.
On the Machine Learning of Ethical Judgments from Natural Language
Ethics is one of the longest standing intellectual endeavors of humanity.
Grammatical Gender's Influence on Distributional Semantics: A Causal Perspective
However, when we control for the meaning of the noun, we find that grammatical gender has a near-zero effect on adjective choice, thereby calling the neo-Whorfian hypothesis into question.
ROBBIE: Robust Bias Evaluation of Large Generative Language Models
In this work, our focus is two-fold: (1) Benchmarking: a comparison of 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative LLMs.
The Validity of Evaluation Results: Assessing Concurrence Across Compositionality Benchmarks
NLP models have progressed drastically in recent years, according to numerous datasets proposed to evaluate performance.
The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languages
We showcase it to report gender representation in WMT training data and development data for the News task, confirming that current data is skewed towards masculine representation.
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
The unprecedented photorealistic results achieved by recent text-to-image generative systems and their increasing use as plug-and-play content creation solutions make it crucial to understand their potential biases.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #1 on
on
Weisfeiler and Lehman Go Measurement Modeling: Probing the Validity of the WL Test
The expressive power of graph neural networks is usually measured by comparing how many pairs of graphs or nodes an architecture can possibly distinguish as non-isomorphic to those distinguishable by the $k$-dimensional Weisfeiler-Lehman ($k$-WL) test.
Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
In partnership with CoNLL and CMCL, we provide a platform for approaches to pretraining with a limited-size corpus sourced from data inspired by the input to children.
Language model acceptability judgements are not always robust to context
In this paper, we investigate the stability of language models' performance on targeted syntactic evaluations as we vary properties of the input context: the length of the context, the types of syntactic phenomena it contains, and whether or not there are violations of grammaticality.
The Curious Case of Absolute Position Embeddings
Transformer language models encode the notion of word order using positional information.
Benchmarking Compositionality with Formal Languages
Recombining known primitive concepts into larger novel combinations is a quintessentially human cognitive capability.
Perturbation Augmentation for Fairer NLP
Unwanted and often harmful social biases are becoming ever more salient in NLP research, affecting both models and datasets.
"I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset
As language models grow in popularity, it becomes increasingly important to clearly measure all possible markers of demographic identity 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.
Ranked #28 on
Visual Reasoning
on 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.
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).
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.
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.
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.
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.
Cross-Lingual Natural Language Inference
Machine Translation
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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.
