33.239, FYI: Monthly Online ILFC Seminar: interactions between formal and computational linguistics

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LINGUIST List: Vol-33-239. Fri Jan 21 2022. ISSN: 1069 - 4875.

Subject: 33.239, FYI: Monthly Online ILFC Seminar: interactions between formal and computational linguistics

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Date: Fri, 21 Jan 2022 22:44:54
From: Timothée Bernard [timothee.bernard at u-paris.fr]
Subject: Monthly Online ILFC Seminar: interactions between formal and computational linguistics

 
Monthly online ILFC Seminar: interactions between formal and computational
linguistics
https://gdr-lift.loria.fr/monthy-online-ilfc-seminar/

GdR LIFT is happy to announce the forthcoming sessions of the ILFC seminar on
the interactions between formal and computational linguistics:

2022/02/15 17:00-18:00 UTC+1: Najoung Kim (New York University; 11:00-12:00
UTC-5)

Title:
Compositional Linguistic Generalization in Artificial Neural Networks

Abstract:
Compositionality is considered a central property of human language. One key
benefit of compositionality is the generalization it enables—the production
and comprehension of novel expressions analyzed as new compositions of
familiar parts. I construct a test for compositional generalization for
artificial neural networks based on human generalization patterns discussed in
existing linguistic and developmental studies, and test several instantiations
of Transformer (Vaswani et al. 2017) and Long Short-Term Memory (Hochreiter &
Schmidhuber 1997) models. The models evaluated exhibit only limited degrees of
compositional generalization, implying that their learning biases for
induction to fill gaps in the training data differ from those of human
learners. An error analysis reveals that all models tested lack bias towards
faithfulness (à la Prince & Smolensky 1993/2002). Adding a glossing task
(word-by-word translation), a task that requires maximally faithful
input-output mappings, as an auxiliary training objective to the Transformer
model substantially improves generalization, showing that the auxiliary
training successfully modified the model’s inductive bias. However, the
improvement is limited to generalization to novel compositions of known
lexical items and known structures; all models still struggled with
generalization to novel structures, regardless of auxiliary training. The
challenge of structural generalization leaves open exciting avenues for future
research for both human and machine learners.

2022/04/12 15:00-16:00 UTC+1: Noortje Venhuizen (Saarland University)
Title: Distributional Formal Semantics
Abstract: [TBA]

The seminar is held on Zoom. To attend the seminar and get updates, please
register to be on our mailing list:
https://sympa.inria.fr/sympa/subscribe/seminaire_ilfc
 



Linguistic Field(s): Computational Linguistics
                     Semantics





 



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