Search Results for author: Lenhart Schubert

Found 20 papers, 2 papers with code

Learning General Event Schemas with Episodic Logic

no code implementations ACL (NALOMA, IWCS) 2021 Lane Lawley, Benjamin Kuehnert, Lenhart Schubert

We present a system for learning generalized, stereotypical patterns of events—or “schemas”—from natural language stories, and applying them to make predictions about other stories.

World Knowledge

A (Mostly) Symbolic System for Monotonic Inference with Unscoped Episodic Logical Forms

no code implementations ACL (NALOMA, IWCS) 2021 Gene Kim, Mandar Juvekar, Junis Ekmekciu, Viet Duong, Lenhart Schubert

We implement the formalization of natural logic-like monotonic inference using Unscoped Episodic Logical Forms (ULFs) by Kim et al. (2020).

Monotonic Inference for Underspecified Episodic Logic

no code implementations ACL (NALOMA, IWCS) 2021 Gene Kim, Mandar Juvekar, Lenhart Schubert

We present a method of making natural logic inferences from Unscoped Logical Form of Episodic Logic.

Logical Story Representations via FrameNet + Semantic Parsing

no code implementations NAACL (DistCurate) 2022 Lane Lawley, Lenhart Schubert

We propose a means of augmenting FrameNet parsers with a formal logic parser to obtain rich semantic representations of events.

Formal Logic Question Answering +1

We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses

1 code implementation10 Oct 2023 Benjamin Kane, Lenhart Schubert

We capture such habitual knowledge using an explicit schema representation, and propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate persona-based responses.

Dialogue Generation Language Modelling +1

Get the gist? Using large language models for few-shot decontextualization

no code implementations10 Oct 2023 Benjamin Kane, Lenhart Schubert

In many NLP applications that involve interpreting sentences within a rich context -- for instance, information retrieval systems or dialogue systems -- it is desirable to be able to preserve the sentence in a form that can be readily understood without context, for later reuse -- a process known as ``decontextualization''.

Information Retrieval Language Modelling +3

A Flexible Schema-Guided Dialogue Management Framework: From Friendly Peer to Virtual Standardized Cancer Patient

no code implementations15 Jul 2022 Benjamin Kane, Catherine Giugno, Lenhart Schubert, Kurtis Haut, Caleb Wohn, Ehsan Hoque

A schema-guided approach to dialogue management has been shown in recent work to be effective in creating robust customizable virtual agents capable of acting as friendly peers or task assistants.

Dialogue Management Management

Mining Logical Event Schemas From Pre-Trained Language Models

no code implementations ACL 2022 Lane Lawley, Lenhart Schubert

We present NESL (the Neuro-Episodic Schema Learner), an event schema learning system that combines large language models, FrameNet parsing, a powerful logical representation of language, and a set of simple behavioral schemas meant to bootstrap the learning process.

Language Modelling

A Transition-based Parser for Unscoped Episodic Logical Forms

1 code implementation IWCS (ACL) 2021 Gene Louis Kim, Viet Duong, Xin Lu, Lenhart Schubert

"Episodic Logic:Unscoped Logical Form" (EL-ULF) is a semantic representation capturing predicate-argument structure as well as more challenging aspects of language within the Episodic Logic formalism.

Generating Discourse Inferences from Unscoped Episodic Logical Formulas

no code implementations WS 2019 Gene Kim, Benjamin Kane, Viet Duong, Muskaan Mendiratta, Graeme McGuire, Sophie Sackstein, Georgiy Platonov, Lenhart Schubert

Abstract Unscoped episodic logical form (ULF) is a semantic representation capturing the predicate-argument structure of English within the episodic logic formalism in relation to the syntactic structure, while leaving scope, word sense, and anaphora unresolved.

Towards Natural Language Story Understanding with Rich Logical Schemas

no code implementations WS 2019 Gene Louis Kim, Lane Lawley, Lenhart Schubert

The idea of our approach to this problem is to provide a learning system with a {``}head start{''} consisting of a semantic parser, some basic ontological knowledge, and most importantly, a small set of very general schemas about the kinds of patterns of events (often purposive, causal, or socially conventional) that even a one- or two-year-old could reasonably be presumed to possess.

A Type-coherent, Expressive Representation as an Initial Step to Language Understanding

no code implementations WS 2019 Gene Louis Kim, Lenhart Schubert

A growing interest in tasks involving language understanding by the NLP community has led to the need for effective semantic parsing and inference.

Semantic Parsing

Computational Models for Spatial Prepositions

no code implementations WS 2018 Georgiy Platonov, Lenhart Schubert

However, what really matters pragmatically is not the accuracy of truth value judgments but whether, for instance, the computer models suffice for identifying objects described in terms of prepositional relations, (e. g., {``}the box to the left of the table{''}, where there are multiple boxes).

Intension, Attitude, and Tense Annotation in a High-Fidelity Semantic Representation

no code implementations WS 2017 Gene Kim, Lenhart Schubert

EL has proved competitive with other logical formulations in speed and inference-enablement, while expressing a wider array of natural language phenomena including intensional modification of predicates and sentences, propositional attitudes, and tense and aspect.

Vocal Bursts Intensity Prediction

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