32.2997, FYI: Monthly Online ILFC Seminar: Interactions between Formal and Computational Linguistics

[The LINGUIST List]

LINGUIST List: Vol-32-2997. Wed Sep 22 2021. ISSN: 1069 - 4875.

Subject: 32.2997, FYI: Monthly Online ILFC Seminar: Interactions between Formal and Computational Linguistics

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Date: Wed, 22 Sep 2021 10:14:32
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 two forthcoming sessions of the ILFC seminar
on the interactions between formal and computational linguistics:
2021/10/12 17:00-18:00 UTC+2: Christopher Potts (Stanford University;
8:00-9:00 UTC-7)

Title: Causal Abstractions of Neural Natural Language Inference Models
Abstract: Neural networks have a reputation for being "black boxes" — complex,
opaque systems that can be studied using only purely behavioral evaluations.
However, much recent work on structural analysis methods (e.g., probing and
feature attribution) is allowing us to peer inside these models and deeply
understand their internal dynamics. In this talk, I'll describe a new
structural analysis method we've developed that is grounded in a formal theory
of causal abstraction. In this method, neural representations are aligned with
variables in interpretable causal models, and then *interchange interventions*
are used to experimentally verify that the neural representations have the
causal properties of their aligned variables. I'll use these methods to
explore problems in Natural Language Inference, focusing in particular on
compositional interactions between lexical entailment and negation. Recent
Transformer-based models can solve hard generalization tasks involving these
phenomena, and our causal analysis method helps explain why: the models have
learned modular representations that closely approximate the high-level
compositional theory. Finally, I will show how to bring interchange
interventions into the training process, which allows us to push our models to
acquire desired modular internal structures like this.
 
Joint work with Atticus Geiger, Hanson Lu, Noah Goodman, and Thomas Icard

2021/11/16 17:00-18:00 UTC+2: Alex Lascarides (University of Edinburgh;
16:00-17:00 UTC+1)
Title: Situated Communication

Abstract: This talk focuses on how to represent and reason about the content
of conversation when it takes place in an embodied, dynamic environment. I
will argue that speakers can, and do, appropriate non-linguistic events into
their communicative intents, even when those events weren't produced with the
intention of being a part of a discourse. Indeed, non-linguistic events can
contribute an (instance of) a proposition to the content of the speaker's
message, even when her verbal signal contains no demonstratives or anaphora of
any kind.

I will argue that representing and reasoning about discourse coherence is
essential to capturing these features of situated conversation. I will make
two claims: first, non-linguistic events affect rhetorical structure in
non-trivial ways; and secondly, rhetorical structure guides the
conceptualisation of non-linguistic events. I will support the first claim via
empirical observations from the STAC corpus (www.irit.fr/STAC/corpus.html)---a
corpus of dialogues that take place between players during the board game
Settlers of Catan. I will support the second claim via experiments in
Interactive Task Learning: a software agent jointly learns how to
conceptualise the domain, ground previously unknown words in the embodied
environment, and solve its planning problem, by using the evidence of an
expert's corrective (verbal) feedback on its physical actions.

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
                     Discourse Analysis
                     Semantics





 



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