23 research outputs found
Constructive Type-Logical Supertagging with Self-Attention Networks
We propose a novel application of self-attention networks towards grammar
induction. We present an attention-based supertagger for a refined type-logical
grammar, trained on constructing types inductively. In addition to achieving a
high overall type accuracy, our model is able to learn the syntax of the
grammar's type system along with its denotational semantics. This lifts the
closed world assumption commonly made by lexicalized grammar supertaggers,
greatly enhancing its generalization potential. This is evidenced both by its
adequate accuracy over sparse word types and its ability to correctly construct
complex types never seen during training, which, to the best of our knowledge,
was as of yet unaccomplished.Comment: REPL4NLP 4, ACL 201
Neural Proof Nets
Linear logic and the linear {\lambda}-calculus have a long standing tradition
in the study of natural language form and meaning. Among the proof calculi of
linear logic, proof nets are of particular interest, offering an attractive
geometric representation of derivations that is unburdened by the bureaucratic
complications of conventional prooftheoretic formats. Building on recent
advances in set-theoretic learning, we propose a neural variant of proof nets
based on Sinkhorn networks, which allows us to translate parsing as the problem
of extracting syntactic primitives and permuting them into alignment. Our
methodology induces a batch-efficient, end-to-end differentiable architecture
that actualizes a formally grounded yet highly efficient neuro-symbolic parser.
We test our approach on {\AE}Thel, a dataset of type-logical derivations for
written Dutch, where it manages to correctly transcribe raw text sentences into
proofs and terms of the linear {\lambda}-calculus with an accuracy of as high
as 70%.Comment: 14 pages, CoNLL202
A Logic-Based Framework for Natural Language Inference in Dutch
We present a framework for deriving inference relations between Dutch sentence pairs. The proposed framework relies on logic-based reasoning to produce inspectable proofs leading up to inference labels; its judgements are therefore transparent and formally verifiable. At its core, the system is powered by two λ-calculi, used as syntactic and semantic theories, respectively. Sentences are first converted to syntactic proofs and terms of the linear λ-calculus using a choice of two parsers: an Alpino-based pipeline, and Neural Proof Nets. The syntactic terms are then converted to semantic terms of the simply typed λ-calculus, via a set of hand designed type- and term-level transformations. Pairs of semantic terms are then fed to an automated theorem prover for natural logic which reasons with them while using lexical relations found in the Open Dutch WordNet. We evaluate the reasoning pipeline on the recently created Dutch natural language inference dataset, and achieve promising results, remaining only within a 1.1−3.2% performance margin to strong neural baselines. To the best of our knowledge, the reasoning pipeline is the first logic-based system for Dutch
A Logic-Based Framework for Natural Language Inference in Dutch
We present a framework for deriving inference relations between Dutch sentence pairs. The proposed framework relies on logic-based reasoning to produce inspectable proofs leading up to inference labels; its judgements are therefore transparent and formally verifiable. At its core, the system is powered by two λ-calculi, used as syntactic and semantic theories, respectively. Sentences are first converted to syntactic proofs and terms of the linear λ-calculus using a choice of two parsers: an Alpino-based pipeline, and Neural Proof Nets. The syntactic terms are then converted to semantic terms of the simply typed λ-calculus, via a set of hand designed type- and term-level transformations. Pairs of semantic terms are then fed to an automated theorem prover for natural logic which reasons with them while using lexical relations found in the Open Dutch WordNet. We evaluate the reasoning pipeline on the recently created Dutch natural language inference dataset, and achieve promising results, remaining only within a 1.1−3.2% performance margin to strong neural baselines. To the best of our knowledge, the reasoning pipeline is the first logic-based system for Dutch
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl