All speakers can understand a sentence never heard before, or derive the
meaning of a word or a sentence from its parts. Children can learn any
language they are exposed to. And yet, these basic linguistic skills
have proven very hard to reach by computational models. The current
reported success of machine learning architectures is based on
computationally expensive algorithms and prohibitively large amounts of
data that are available for only a few, non-representative languages.
This limits the access to natural language processing technology to a
few dominant languages and modalities and leads to the development of
systems that are not human-like, with great potential for unfairness and
bias. To reach better, possibly human-like, abilities in neural
networks' abstraction and generalization, we need to move beyond the
simple language modelling tasks currently used and develop tasks and
data that train the networks to more complex and compositional
linguistic skills.In this project, we set the challenging goals of
achieving higher-level linguistic abilities in machines, while training
in more realistic settings. We identify these abilities as (i) the
intelligent ability to infer patterns of regularities in unstructured
data, (ii) generalise from few examples, using (iii) abstractions that
are valid across possibly very different languages.We study if current
neural network architectures have the same properties of learning,
generalization, and abstraction when processing language. Specifically,
we ask (i) Can they learn the underlying generative structure
of complex data? (ii) Under what conditions can they learn from
zero or few examples? (iii) Do their learning patterns exhibit
cross-linguistically valid abstractions?
To achieve these goals, we
concentrate on one of the core building blocks of any language: verbs
and their argument structure, the 'who did what to whom' expressing
core events and actions. Argument structure is defined by specific
combinations of elements (the arguments and the predicate) in different
templates (the subcategorization frames), that form higher-order
patterns of similarity across sentences (the alternations) and are
defined at a high level of abstraction across languages (the semantic
roles).We aim to learn disentangled representations of these components
of argument structure. A disentangled representation encodes
information about the salient factors of variation in the data
independently. We have developed, and demonstrated in pilot work, a new
set of progressive matrix tasks, inspired by IQ intelligence tests.
These tasks are developed specifically for language and learning
disentangled linguistic representations of underlying linguistic rules
of grammar.
We apply this novel method to the three main questions of
our investigation: (i) To demonstrate learning of the generative
components of argument structure from data and facets of verb meaning,
we develop new data sets and new versions of the progressive matrix task
for argument structure concentrating specifically on learning argument
alternations (John loaded the truck with hay/John loaded Hay onto the
truck), on the gradation of compositionality (obligatory arguments vs
optional adjuncts) and the compositionality of complex clauses. (ii) To
investigate the conditions that make learning from few examples
possible, we hypothesize that structured categorization reduces the data
sample size needed for learning. We also take inspiration from human
learning biases and develop models of increasing complexity and size to
study how structure and data size interact, and in so doing learn
solutions for low-resource languages. (iii) To study if truly abstract
representations of verb argument structure and semantic roles emerge, we
create novel progressive matrix learning tasks and novel cross-lingual
artificial complex data of complex (non-concatenative) morphological
paradigms in typologically different languages.(iv) We develop novel
computational architectures and novel evaluation metrics adapted to
these problems that exhibit a mixture of linguistic abilities and
logical coherence.This kind of investigation, based on tasks that
require a mixture of linguistic knowledge and higher-level linguistic
reasoning has never been tried before for natural language processing.
Current pilot studies for simple linguistic problems by the PI and her
team show that the method is promising. We plan to extend the method
here to two very challenging areas of natural language processing. On
the one hand, we tackle complex linguistic data that tap into the core
semantics of clauses, a well-studied, but so far unsolved, core problem
for natural language understanding, for which the PI is a leading
expert. On the other hand, we study complex morphological paradigms and
systems, a very little studied cross-linguistically important problem.If
successful, this research could lead to a significant methodological
shift. These investigations can lead to three beneficial improvements of
methods and practices: (i) deep, compositional representations would be
learned, thus reducing needs in data size; (ii) current machine
learning methods would be extended to low-resources languages or
low-resource modalities and scenarios; (iii) higher-level abstractions
would be learned, avoiding the use of superficial, associative cues that
are the cause of so much bias and potential harm in the representations
learned by current artificial intelligence and natural language
processing systems.
