Recent Progress on Monotonicity

By Thomas F. Icard III, Larry Moss. 2014

Subfields: Computational Linguistics & Semantics

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In Computational Linguistics & Semantics

Citations in the corpus

Recursive Neural Networks Can Learn Logical Semantics (2014, background): “The formal properties of this system are now well-understood (Icard and Moss, 2013a; Icard and Moss, 2013b).”
NaturalLI: Natural Logic Inference for Common Sense Reasoning (2014, methodology): “Later work formalizes many aspects of the logic (Icard, 2012; Djalali, 2013); we adopt the formal semantics of Icard and Moss (2014), along with much of their notation.”
Probing Natural Language Inference Models through Semantic Fragments (2019, background): “For our second set of fragments that focus on monotonicity reasoning, the target formal system is based on the monotonicity calculus of van Benthem (1986) (see review by Icard and Moss (2014)).”
A Monotonicity Calculus and Its Completeness (2017, unknown): “(For review see Icard and Moss 2014.)”
Recursive Neural Networks for Learning Logical Semantics (2014, background): “…the only place in natural language where complex functional meanings are found, but it is a natural starting point, since it can be tested in sentences whose structures are otherwise quite simple, and since it has formed a standard case study in prior formal work on natural language inference [18].”
AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples (2018, background): “The composition function is the same as the “join” operation in natural logic reasoning (Icard III and Moss, 2014), except for two differences: (a) relations that do not belong to our three entailment classes are mapped to ‘?’”
From Natural Logic to Natural Reasoning (2015, methodology): “Among the latest advances is the work by Jan van Eijck [8], Bill MacCartney [20] and the recent papers by Thomas Icard [10] and Larry Moss [11] that build on the work of MacCartney and Christopher Manning [21].”
A Natural Proof System for Natural Language (2017, methodology): “In our case, similarly to Icard (2012); Icard and Moss (2014), x|y will mean only x ∩ y = ∅ and x ` y only x ∪ y = U .”
Exploring End-to-End Differentiable Natural Logic Modeling (2020, background): “Natural logic instead aims to sidestep some of the challenges by performing inferences over surface forms of text based on monotonicity or projectivity (Van Benthem, 1986; Valencia, 1991; MacCartney and Manning, 2009; Icard and Moss, 2014), and has been applied to tasks such as natural language infe”
Leveraging Linguistic Structure For Open Domain Information Extraction (2015, methodology): “We adopt the precise semantics of Icard and Moss (2014).”
Causal Abstractions of Neural Networks (2021, methodology): “This challenging dataset was constructed with a tree-structured natural logic causal model [17, 29, 14].”
Can Neural Networks Understand Monotonicity Reasoning? (2019, background): “Concerning logical inferences, monotonicity reasoning (van Benthem, 1983; Icard and Moss, 2014), which is a type of reasoning based on word replacement, requires the ability to capture the interaction between lexical and syntactic structures.”
Combining Natural Logic and Shallow Reasoning for Question Answering (2016, methodology): “We adopt the precise semantics of Icard and Moss (2014); we refer the reader to this paper for a more thorough introduction to the formalism.”