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Found 33 papers, 18 papers with code
Annotating Japanese Numeral Expressions for a Logical and Pragmatic Inference Dataset
We also construct an inference test set for numerical expressions based on this annotated corpus.
Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference
Despite the success of multilingual pre-trained language models, it remains unclear to what extent these models have human-like generalization capacity across languages.
Natural Language Inference
Out-of-Distribution Generalization
Computational Semantics and Evaluation Benchmark for Interrogative Sentences via Combinatory Categorial Grammar
We present a compositional semantics for various types of polar questions and wh-questions within the framework of Combinatory Categorial Grammar (CCG).
Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases
Our findings demonstrate that current large language models struggle more with problems involving these three types of biases.
Compositional Evaluation on Japanese Textual Entailment and Similarity
We also present a stress-test dataset for compositional inference, created by transforming syntactic structures of sentences in JSICK to investigate whether language models are sensitive to word order and case particles.
Building a Video-and-Language Dataset with Human Actions for Multimodal Logical Inference
This paper introduces a new video-and-language dataset with human actions for multimodal logical inference, which focuses on intentional and aspectual expressions that describe dynamic human actions.
SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics
We also find that the generalization performance to unseen combinations is better when the form of meaning representations is simpler.
Visual representation of negation: Real world data analysis on comic image design
There has been a widely held view that visual representations (e. g., photographs and illustrations) do not depict negation, for example, one that can be expressed by a sentence "the train is not coming".
Exploring Transitivity in Neural NLI Models through Veridicality
Despite the recent success of deep neural networks in natural language processing, the extent to which they can demonstrate human-like generalization capacities for natural language understanding remains unclear.
Combining Event Semantics and Degree Semantics for Natural Language Inference
In formal semantics, there are two well-developed semantic frameworks: event semantics, which treats verbs and adverbial modifiers using the notion of event, and degree semantics, which analyzes adjectives and comparatives using the notion of degree.
Logical Inferences with Comparatives and Generalized Quantifiers
Comparative constructions pose a challenge in Natural Language Inference (NLI), which is the task of determining whether a text entails a hypothesis.
Development of a General-Purpose Categorial Grammar Treebank
This paper introduces ABC Treebank, a general-purpose categorial grammar (CG) treebank for Japanese.
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language?
This indicates that the generalization ability of neural models is limited to cases where the syntactic structures are nearly the same as those in the training set.
A CCG-based Compositional Semantics and Inference System for Comparatives
Comparative constructions play an important role in natural language inference.
Can neural networks understand monotonicity reasoning?
Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures.
Multimodal Logical Inference System for Visual-Textual Entailment
A large amount of research about multimodal inference across text and vision has been recently developed to obtain visually grounded word and sentence representations.
Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation
We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees.
Questions in Dependent Type Semantics
The basic idea is to assign the same type to both declarative sentences and interrogative sentences, partly building on the recent proposal in Inquisitive Semantics.
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning
To investigate this issue, we introduce a new dataset, called HELP, for handling entailments with lexical and logical phenomena.
Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference
In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data.
Neural sentence generation from formal semantics
In this paper, we present a sequence-to-sequence model for generating sentences from logical meaning representations based on event semantics.
Acquisition of Phrase Correspondences using Natural Deduction Proofs
How to identify, extract, and use phrasal knowledge is a crucial problem for the task of Recognizing Textual Entailment (RTE).
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning
In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas.
The Challenge of Composition in Distributional and Formal Semantics
Only a limited part of the contents in this tutorial is drawn from the previous one.
Visual Denotations for Recognizing Textual Entailment
In the logic approach to Recognizing Textual Entailment, identifying phrase-to-phrase semantic relations is still an unsolved problem.
Determining Semantic Textual Similarity using Natural Deduction Proofs
Determining semantic textual similarity is a core research subject in natural language processing.
On-demand Injection of Lexical Knowledge for Recognising Textual Entailment
We approach the recognition of textual entailment using logical semantic representations and a theorem prover.
Annotation and Analysis of Discourse Relations, Temporal Relations and Multi-Layered Situational Relations in Japanese Texts
This paper proposes a methodology for building a specialized Japanese data set for recognizing temporal relations and discourse relations.
