Lenhart Schubert

Also published as: Len Schubert, Lenhart K. Schubert


2021

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Modeling Semantics and Pragmatics of Spatial Prepositions via Hierarchical Common-Sense Primitives
Georgiy Platonov | Yifei Yang | Haoyu Wu | Jonathan Waxman | Marcus Hill | Lenhart Schubert
Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics

Understanding spatial expressions and using them appropriately is necessary for seamless and natural human-machine interaction. However, capturing the semantics and appropriate usage of spatial prepositions is notoriously difficult, because of their vagueness and polysemy. Although modern data-driven approaches are good at capturing statistical regularities in the usage, they usually require substantial sample sizes, often do not generalize well to unseen instances and, most importantly, their structure is essentially opaque to analysis, which makes diagnosing problems and understanding their reasoning process difficult. In this work, we discuss our attempt at modeling spatial senses of prepositions in English using a combination of rule-based and statistical learning approaches. Each preposition model is implemented as a tree where each node computes certain intuitive relations associated with the preposition, with the root computing the final value of the prepositional relation itself. The models operate on a set of artificial 3D “room world” environments, designed in Blender, taking the scene itself as an input. We also discuss our annotation framework used to collect human judgments employed in the model training. Both our factored models and black-box baseline models perform quite well, but the factored models will enable reasoned explanations of spatial relation judgements.

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Generating Justifications in a Spatial Question-Answering Dialogue System for a Blocks World
Georgiy Platonov | Benjamin Kane | Lenhart Schubert
Proceedings of the Reasoning and Interaction Conference (ReInAct 2021)

As AI reaches wider adoption, designing systems that are explainable and interpretable becomes a critical necessity. In particular, when it comes to dialogue systems, their reasoning must be transparent and must comply with human intuitions in order for them to be integrated seamlessly into day-to-day collaborative human-machine activities. Here, we describe our ongoing work on a (general purpose) dialogue system equipped with a spatial specialist with explanatory capabilities. We applied this system to a particular task of characterizing spatial configurations of blocks in a simple physical Blocks World (BW) domain using natural locative expressions, as well as generating justifications for the proposed spatial descriptions by indicating the factors that the system used to arrive at a particular conclusion.

2020

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A Spoken Dialogue System for Spatial Question Answering in a Physical Blocks World
Georgiy Platonov | Lenhart Schubert | Benjamin Kane | Aaron Gindi
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

A physical blocks world, despite its relative simplicity, requires (in fully interactive form) a rich set of functional capabilities, ranging from vision to natural language understanding. In this work we tackle spatial question answering in a holistic way, using a vision system, speech input and output mediated by an animated avatar, a dialogue system that robustly interprets spatial queries, and a constraint solver that derives answers based on 3-D spatial modeling. The contributions of this work include a semantic parser that maps spatial questions into logical forms consistent with a general approach to meaning representation, a dialogue manager based on a schema representation, and a constraint solver for spatial questions that provides answers in agreement with human perception. These and other components are integrated into a multi-modal human-computer interaction pipeline.

2019

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A Type-coherent, Expressive Representation as an Initial Step to Language Understanding
Gene Louis Kim | Lenhart Schubert
Proceedings of the 13th International Conference on Computational Semantics - Long Papers

A growing interest in tasks involving language understanding by the NLP community has led to the need for effective semantic parsing and inference. Modern NLP systems use semantic representations that do not quite fulfill the nuanced needs for language understanding: adequately modeling language semantics, enabling general inferences, and being accurately recoverable. This document describes underspecified logical forms (ULF) for Episodic Logic (EL), which is an initial form for a semantic representation that balances these needs. ULFs fully resolve the semantic type structure while leaving issues such as quantifier scope, word sense, and anaphora unresolved; they provide a starting point for further resolution into EL, and enable certain structural inferences without further resolution. This document also presents preliminary results of creating a hand-annotated corpus of ULFs for the purpose of training a precise ULF parser, showing a three-person pairwise interannotator agreement of 0.88 on confident annotations. We hypothesize that a divide-and-conquer approach to semantic parsing starting with derivation of ULFs will lead to semantic analyses that do justice to subtle aspects of linguistic meaning, and will enable construction of more accurate semantic parsers.

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Towards Natural Language Story Understanding with Rich Logical Schemas
Gene Louis Kim | Lane Lawley | Lenhart Schubert
Proceedings of the Sixth Workshop on Natural Language and Computer Science

Generating “commonsense’’ knowledge for intelligent understanding and reasoning is a difficult, long-standing problem, whose scale challenges the capacity of any approach driven primarily by human input. Furthermore, approaches based on mining statistically repetitive patterns fail to produce the rich representations humans acquire, and fall far short of human efficiency in inducing knowledge from text. 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. We match these initial schemas to simple children’s stories, obtaining concrete instances, and combining and abstracting these into new candidate schemas. Both the initial and generated schemas are specified using a rich, expressive logical form. While modern approaches to schema reasoning often only use slot-and-filler structures, this logical form allows us to specify complex relations and constraints over the slots. Though formal, the representations are language-like, and as such readily relatable to NL text. The agents, objects, and other roles in the schemas are represented by typed variables, and the event variables can be related through partial temporal ordering and causal relations. To match natural language stories with existing schemas, we first parse the stories into an underspecified variant of the logical form used by the schemas, which is suitable for most concrete stories. We include a walkthrough of matching a children’s story to these schemas and generating inferences from these matches.

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Generating Discourse Inferences from Unscoped Episodic Logical Formulas
Gene Kim | Benjamin Kane | Viet Duong | Muskaan Mendiratta | Graeme McGuire | Sophie Sackstein | Georgiy Platonov | Lenhart Schubert
Proceedings of the First International Workshop on Designing Meaning Representations

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. We describe how ULF can be used to generate natural language inferences that are grounded in the semantic and syntactic structure through a small set of rules defined over interpretable predicates and transformations on ULFs. The semantic restrictions placed by ULF semantic types enables us to ensure that the inferred structures are semantically coherent while the nearness to syntax enables accurate mapping to English. We demonstrate these inferences on four classes of conversationally-oriented inferences in a mixed genre dataset with 68.5% precision from human judgments.

2018

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Computational Models for Spatial Prepositions
Georgiy Platonov | Lenhart Schubert
Proceedings of the First International Workshop on Spatial Language Understanding

Developing computational models of spatial prepositions (such as on, in, above, etc.) is crucial for such tasks as human-machine collaboration, story understanding, and 3D model generation from descriptions. However, these prepositions are notoriously vague and ambiguous, with meanings depending on the types, shapes and sizes of entities in the argument positions, the physical and task context, and other factors. As a result truth value judgments for prepositional relations are often uncertain and variable. In this paper we treat the modeling task as calling for assignment of probabilities to such relations as a function of multiple factors, where such probabilities can be viewed as estimates of whether humans would judge the relations to hold in given circumstances. We implemented our models in a 3D blocks world and a room world in a computer graphics setting, and found that true/false judgments based on these models do not differ much more from human judgments that the latter differ from one another. 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). For such tasks, our models achieved accuracies above 90% for most relations.

2017

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Intension, Attitude, and Tense Annotation in a High-Fidelity Semantic Representation
Gene Kim | Lenhart Schubert
Proceedings of the Workshop Computational Semantics Beyond Events and Roles

This paper describes current efforts in developing an annotation schema and guidelines for sentences in Episodic Logic (EL). We focus on important distinctions for representing modality, attitudes, and tense and present an annotation schema that makes these distinctions. 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.

2016

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High-Fidelity Lexical Axiom Construction from Verb Glosses
Gene Kim | Lenhart Schubert
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

2014

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From Treebank Parses to Episodic Logic and Commonsense Inference
Lenhart Schubert
Proceedings of the ACL 2014 Workshop on Semantic Parsing

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NLog-like Inference and Commonsense Reasoning
Lenhart Schubert
Linguistic Issues in Language Technology, Volume 9, 2014 - Perspectives on Semantic Representations for Textual Inference

Recent implementations of Natural Logic (NLog) have shown that NLog provides a quite direct means of going from sentences in ordinary language to many of the obvious entailments of those sentences. We show here that Episodic Logic (EL) and its Epilog implementation are well-adapted to capturing NLog-like inferences, but beyond that, also support inferences that require a combination of lexical knowledge and world knowledge. However, broad language understanding and commonsense reasoning are still thwarted by the “knowledge acquisition bottleneck”, and we summarize some of our ongoing and contemplated attacks on that persistent difficulty.

2012

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TTT: A Tree Transduction Language for Syntactic and Semantic Processing
Adam Purtee | Lenhart Schubert
Proceedings of the Workshop on Applications of Tree Automata Techniques in Natural Language Processing

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Using Textual Patterns to Learn Expected Event Frequencies
Jonathan Gordon | Lenhart Schubert
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)

2011

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Discovering Commonsense Entailment Rules Implicit in Sentences
Jonathan Gordon | Lenhart Schubert
Proceedings of the TextInfer 2011 Workshop on Textual Entailment

2010

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Evaluation of Commonsense Knowledge with Mechanical Turk
Jonathan Gordon | Benjamin Van Durme | Lenhart Schubert
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

2009

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Deriving Generalized Knowledge from Corpora Using WordNet Abstraction
Benjamin Van Durme | Phillip Michalak | Lenhart Schubert
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Building a Semantic Lexicon of English Nouns via Bootstrapping
Ting Qian | Benjamin Van Durme | Lenhart Schubert
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium

2008

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Open Knowledge Extraction through Compositional Language Processing
Benjamin Van Durme | Lenhart Schubert
Semantics in Text Processing. STEP 2008 Conference Proceedings

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Class-Driven Attribute Extraction
Benjamin Van Durme | Ting Qian | Lenhart Schubert
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2003

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Extracting and evaluating general world knowledge from the Brown Corpus
Lenhart Schubert | Matthew Tong
Proceedings of the HLT-NAACL 2003 Workshop on Text Meaning

1999

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Guiding a Well-Founded Parser with Corpus Statistics
Amon Seagull | Lenhart Schubert
1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

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A Syntactic Framework for Speech Repairs and Other Disruptions
Mark G. Core | Lenhart K. Schubert
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics

1994

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Natural Language Planning Dialogue for Interactive
James F. Allen | Len Schubert
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

1993

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Interpreting Temporal Adverbials
Chung Hee Hwang | Lenhart K. Schubert
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Natural Language Planning Dialogue for Intelligent Applications
James F. Allen | Len Schubert
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

1992

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Tense Trees as the “Fine Structure” of Discourse
Chung Hee Hwang | Lenhart K. Schubert
30th Annual Meeting of the Association for Computational Linguistics

1991

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Natural Language, Knowledge Representation and Discourse
James F. Allen | Lenhart K. Schubert
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

1990

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Picking Reference Events from Tense A Formal, Implement able Theory of English Tense-Aspect Semantics Trees:
Lenhart K. Schubert | Chung Hee Hwang
Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27,1990

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Natural Language, Knowledge Representation, and Discourse
James F. Allen | Lenhart K. Schubert
Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27,1990

1989

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Natural Language, Knowledge Representation and Discourse
James F. Allen | Lenhart K. Schubert
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989

1984

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Two Theories for Computing the Logical Form of Mass Expressions
Francis Jeffry Pelletier | Lenhart K. Schubert
10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics

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On Parsing Preferences
Lenhart K. Schubert
10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics

1982

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From English to Logic: Context-Free Computation of ‘Conventional’ Logical Translation
Lenhart K. Schubert | Francis Jeffry Pelletier
American Journal of Computational Linguistics, Volume 8, Number 1, January-March 1982