Search Results for author:
Found 17 papers, 8 papers with code
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.
Causal Analysis of Syntactic Agreement Neurons in Multilingual Language Models
However, much of this analysis has focused on monolingual models, and analyses of multilingual models have employed correlational methods that are confounded by the choice of probing tasks.
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey
We present the results of the NLP Community Metasurvey.
Label Semantic Aware Pre-training for Few-shot Text Classification
Label semantic aware systems have leveraged this information for improved text classification performance during fine-tuning and prediction.
Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models
We find that pre-trained seq2seq models generalize hierarchically when performing syntactic transformations, whereas models trained from scratch on syntactic transformations do not.
Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models
Targeted syntactic evaluations have demonstrated the ability of language models to perform subject-verb agreement given difficult contexts.
Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling
Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models.
Decoding Methods for Neural Narrative Generation
Narrative generation is an open-ended NLP task in which a model generates a story given a prompt.
Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement
The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence.
Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages
Exploiting the broad translation of the Bible into the world{'}s languages, we train and distribute morphosyntactic tools for approximately one thousand languages, vastly outstripping previous distributions of tools devoted to the processing of inflectional morphology.
An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages
We find that best practices in this domain are highly language-specific: adding more languages to a training set is often better, but too many harms performance{---}the best number depends on the source language.
The Johns Hopkins University Bible Corpus: 1600+ Tongues for Typological Exploration
The corpus consists of over 4000 unique translations of the Christian Bible and counting.
Cross-Linguistic Syntactic Evaluation of Word Prediction Models
On other constructions, agreement accuracy was generally higher in languages with richer morphology.
Modeling Color Terminology Across Thousands of Languages
There is an extensive history of scholarship into what constitutes a "basic" color term, as well as a broadly attested acquisition sequence of basic color terms across many languages, as articulated in the seminal work of Berlin and Kay (1969).
Quantity doesn't buy quality syntax with neural language models
We investigate to what extent these shortcomings can be mitigated by increasing the size of the network and the corpus on which it is trained.
