Logical Inferences with Comparatives and Generalized Quantifiers
- URL: http://arxiv.org/abs/2005.07954v1
- Date: Sat, 16 May 2020 11:11:48 GMT
- Title: Logical Inferences with Comparatives and Generalized Quantifiers
- Authors: Izumi Haruta, Koji Mineshima, Daisuke Bekki
- Abstract summary: A logical inference system for comparatives has not been sufficiently developed for use in the Natural Language Inference task.
We present a compositional semantics that maps various comparative constructions in English to semantic representations via Category Grammar (CCG)
We show that the system outperforms previous logic-based systems as well as recent deep learning-based models.
- Score: 18.58482811176484
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Comparative constructions pose a challenge in Natural Language Inference
(NLI), which is the task of determining whether a text entails a hypothesis.
Comparatives are structurally complex in that they interact with other
linguistic phenomena such as quantifiers, numerals, and lexical antonyms. In
formal semantics, there is a rich body of work on comparatives and gradable
expressions using the notion of degree. However, a logical inference system for
comparatives has not been sufficiently developed for use in the NLI task. In
this paper, we present a compositional semantics that maps various comparative
constructions in English to semantic representations via Combinatory Categorial
Grammar (CCG) parsers and combine it with an inference system based on
automated theorem proving. We evaluate our system on three NLI datasets that
contain complex logical inferences with comparatives, generalized quantifiers,
and numerals. We show that the system outperforms previous logic-based systems
as well as recent deep learning-based models.
Related papers
- Detecting and explaining (in)equivalence of context-free grammars [0.6282171844772422]
We propose a scalable framework for deciding, proving, and explaining (in)equivalence of context-free grammars.
We present an implementation of the framework and evaluate it on large data sets collected within educational support systems.
arXiv Detail & Related papers (2024-07-25T17:36:18Z) - Predicting Text Preference Via Structured Comparative Reasoning [110.49560164568791]
We introduce SC, a prompting approach that predicts text preferences by generating structured intermediate comparisons.
We select consistent comparisons with a pairwise consistency comparator that ensures each aspect's comparisons clearly distinguish differences between texts.
Our comprehensive evaluations across various NLP tasks, including summarization, retrieval, and automatic rating, demonstrate that SC equips LLMs to achieve state-of-the-art performance in text preference prediction.
arXiv Detail & Related papers (2023-11-14T18:51:38Z) - A Unifying Framework for Learning Argumentation Semantics [50.69905074548764]
We present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way.
Our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.
arXiv Detail & Related papers (2023-10-18T20:18:05Z) - Modeling Hierarchical Reasoning Chains by Linking Discourse Units and
Key Phrases for Reading Comprehension [80.99865844249106]
We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning.
Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
arXiv Detail & Related papers (2023-06-21T07:34:27Z) - The Better Your Syntax, the Better Your Semantics? Probing Pretrained
Language Models for the English Comparative Correlative [7.03497683558609]
Construction Grammar (CxG) is a paradigm from cognitive linguistics emphasising the connection between syntax and semantics.
We present an investigation of their capability to classify and understand one of the most commonly studied constructions, the English comparative correlative (CC)
Our results show that all three investigated PLMs are able to recognise the structure of the CC but fail to use its meaning.
arXiv Detail & Related papers (2022-10-24T13:01:24Z) - Compositional Semantics and Inference System for Temporal Order based on
Japanese CCG [9.683269364766426]
We present a logic-based Natural Language Inference system that considers temporal order in Japanese.
Our system performs inference involving temporal order by using axioms for temporal relations and automated theorem provers.
We show that our system outperforms previous logic-based systems as well as current deep learning-based models.
arXiv Detail & Related papers (2022-04-20T06:21:21Z) - Compositional Generalization Requires Compositional Parsers [69.77216620997305]
We compare sequence-to-sequence models and models guided by compositional principles on the recent COGS corpus.
We show structural generalization is a key measure of compositional generalization and requires models that are aware of complex structure.
arXiv Detail & Related papers (2022-02-24T07:36:35Z) - Refining Labelled Systems for Modal and Constructive Logics with
Applications [0.0]
This thesis serves as a means of transforming the semantics of a modal and/or constructive logic into an 'economical' proof system.
The refinement method connects two proof-theoretic paradigms: labelled and nested sequent calculi.
The introduced refined labelled calculi will be used to provide the first proof-search algorithms for deontic STIT logics.
arXiv Detail & Related papers (2021-07-30T08:27:15Z) - Discrete representations in neural models of spoken language [56.29049879393466]
We compare the merits of four commonly used metrics in the context of weakly supervised models of spoken language.
We find that the different evaluation metrics can give inconsistent results.
arXiv Detail & Related papers (2021-05-12T11:02:02Z) - Combining Event Semantics and Degree Semantics for Natural Language
Inference [16.536018920603176]
We implement a logic-based NLI system that combines event semantics and degree semantics and their interaction with lexical knowledge.
We evaluate the system on various NLI datasets containing linguistically challenging problems.
arXiv Detail & Related papers (2020-11-02T13:27:21Z) - High-order Semantic Role Labeling [86.29371274587146]
This paper introduces a high-order graph structure for the neural semantic role labeling model.
It enables the model to explicitly consider not only the isolated predicate-argument pairs but also the interaction between the predicate-argument pairs.
Experimental results on 7 languages of the CoNLL-2009 benchmark show that the high-order structural learning techniques are beneficial to the strong performing SRL models.
arXiv Detail & Related papers (2020-10-09T15:33:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.