11 research outputs found
Towards zero-shot language modeling
Can we construct a neural language model which is inductively biased towards learning human language? Motivated by this question, we aim at constructing an informative prior for held-out languages on the task of character-level, open-vocabulary language modeling. We obtain this prior as the posterior over network weights conditioned on the data from a sample of training languages, which is approximated through Laplace’s method. Based on a large and diverse sample of languages, the use of our prior outperforms baseline models with an uninformative prior in both zero-shot and few-shot settings, showing that the prior is imbued with universal linguistic knowledge. Moreover, we harness broad language-specific information available for most languages of the world, i.e., features from typological databases, as distant supervision for held-out languages. We explore several language modeling conditioning techniques, including concatenation and meta-networks for parameter generation. They appear beneficial in the few-shot setting, but ineffective in the zero-shot setting. Since the paucity of even plain digital text affects the majority of the world’s languages, we hope that these insights will broaden the scope of applications for language technology
Universal linguistic inductive biases via meta-learning
How do learners acquire languages from the limited data available to them?
This process must involve some inductive biases - factors that affect how a
learner generalizes - but it is unclear which inductive biases can explain
observed patterns in language acquisition. To facilitate computational modeling
aimed at addressing this question, we introduce a framework for giving
particular linguistic inductive biases to a neural network model; such a model
can then be used to empirically explore the effects of those inductive biases.
This framework disentangles universal inductive biases, which are encoded in
the initial values of a neural network's parameters, from non-universal
factors, which the neural network must learn from data in a given language. The
initial state that encodes the inductive biases is found with meta-learning, a
technique through which a model discovers how to acquire new languages more
easily via exposure to many possible languages. By controlling the properties
of the languages that are used during meta-learning, we can control the
inductive biases that meta-learning imparts. We demonstrate this framework with
a case study based on syllable structure. First, we specify the inductive
biases that we intend to give our model, and then we translate those inductive
biases into a space of languages from which a model can meta-learn. Finally,
using existing analysis techniques, we verify that our approach has imparted
the linguistic inductive biases that it was intended to impart.Comment: To appear in the Proceedings of the 42nd Annual Conference of the
Cognitive Science Societ
Emergent Communication Pretraining for Few-Shot Machine Translation
While state-of-the-art models that rely upon massively multilingual
pretrained encoders achieve sample efficiency in downstream applications, they
still require abundant amounts of unlabelled text. Nevertheless, most of the
world's languages lack such resources. Hence, we investigate a more radical
form of unsupervised knowledge transfer in the absence of linguistic data. In
particular, for the first time we pretrain neural networks via emergent
communication from referential games. Our key assumption is that grounding
communication on images---as a crude approximation of real-world
environments---inductively biases the model towards learning natural languages.
On the one hand, we show that this substantially benefits machine translation
in few-shot settings. On the other hand, this also provides an extrinsic
evaluation protocol to probe the properties of emergent languages ex vitro.
Intuitively, the closer they are to natural languages, the higher the gains
from pretraining on them should be. For instance, in this work we measure the
influence of communication success and maximum sequence length on downstream
performances. Finally, we introduce a customised adapter layer and annealing
strategies for the regulariser of maximum-a-posteriori inference during
fine-tuning. These turn out to be crucial to facilitate knowledge transfer and
prevent catastrophic forgetting. Compared to a recurrent baseline, our method
yields gains of in BLEU score with only NMT
training instances and with NMT training
instances across four language pairs. These proof-of-concept results reveal the
potential of emergent communication pretraining for both natural language
processing tasks in resource-poor settings and extrinsic evaluation of
artificial languages
Graphemic Normalization of the Perso-Arabic Script
Since its original appearance in 1991, the Perso-Arabic script representation
in Unicode has grown from 169 to over 440 atomic isolated characters spread
over several code pages representing standard letters, various diacritics and
punctuation for the original Arabic and numerous other regional orthographic
traditions. This paper documents the challenges that Perso-Arabic presents
beyond the best-documented languages, such as Arabic and Persian, building on
earlier work by the expert community. We particularly focus on the situation in
natural language processing (NLP), which is affected by multiple, often
neglected, issues such as the use of visually ambiguous yet canonically
nonequivalent letters and the mixing of letters from different orthographies.
Among the contributing conflating factors are the lack of input methods, the
instability of modern orthographies, insufficient literacy, and loss or lack of
orthographic tradition. We evaluate the effects of script normalization on
eight languages from diverse language families in the Perso-Arabic script
diaspora on machine translation and statistical language modeling tasks. Our
results indicate statistically significant improvements in performance in most
conditions for all the languages considered when normalization is applied. We
argue that better understanding and representation of Perso-Arabic script
variation within regional orthographic traditions, where those are present, is
crucial for further progress of modern computational NLP techniques especially
for languages with a paucity of resources.Comment: Pre-print to appear in the Proceedings of Grapholinguistics in the
21st Century (G21C), 2022. Telecom Paris, Palaiseau, France, June 8-10, 2022.
41 pages, 38 tables, 3 figure
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Inductive Bias and Modular Design for Sample-Efficient Neural Language Learning
Most of the world's languages suffer from the paucity of annotated data. This curbs the effectiveness of supervised learning, the most widespread approach to modelling language. Instead, an alternative paradigm could take inspiration from the propensity of children to acquire language from limited stimuli, in order to enable machines to learn any new language from a few examples. The abstract mechanisms underpinning this ability include 1) a set of in-born inductive biases and 2) the deep entrenchment of language in other perceptual and cognitive faculties, combined with the ability to transfer and recombine knowledge across these domains. The main contribution of my thesis is giving concrete form to both these intuitions.
Firstly, I argue that endowing a neural network with the correct inductive biases is equivalent to constructing a prior distribution over its weights and its architecture (including connectivity patterns and non-linear activations). This prior is inferred by "reverse-engineering" a representative set of observed languages and harnessing typological features documented by linguists. Thus, I provide a unified framework for cross-lingual transfer and architecture search by recasting them as hierarchical Bayesian neural models.
Secondly, the skills relevant to different language varieties and different tasks in natural language processing are deeply intertwined. Hence, the neural weights modelling the data for each of their combinations can be imagined as lying in a structured space. I introduce a Bayesian generative model of this space, which is factorised into latent variables representing each language and each task. By virtue of this modular design, predictions can generalise to unseen combinations by extrapolating from the data of observed combinations.
The proposed models are empirically validated on a spectrum of language-related tasks (character-level language modelling, part-of-speech tagging, named entity recognition, and common-sense reasoning) and a typologically diverse sample of about a hundred languages. Compared to a series of competitive baselines, they achieve better performances in new languages in zero-shot and few-shot learning settings. In general, they hold promise to extend state-of-the-art language technology to under-resourced languages by means of sample efficiency and robustness to the cross-lingual variation.ERC (Consolidator Grant 648909) Lexical
Google Research Faculty Award 201