28 citations · 60 across the 15 of their papers we have counts for
18 papers
Explaining Textual Entailment with Lexical Entailments: Using LLMs to Supply Lexical Relations for Formal Proofs
Jorryt de Jong, Stefan Moraca, Ettore Cesari +1
Large Language Models (LLMs) are highly capable of natural language reasoning and appear to store a great deal of lexical knowledge, but it is still unclear how much of this knowle…
MERGE: Minimal Expression-Replacement GEneralization Test for Natural Language Inference
Mădălina Zgreabăn, Tejaswini Deoskar, Lasha Abzianidze
As many benchmarks have become saturated, it is increasingly important to create new datasets that evaluate the generalization capacity of current state-of-the-art models in reason…
Formal Proofs as Structured Explanations: Proposing Several Tasks on Explainable Natural Language Inference
Lasha Abzianidze
In this position paper, we propose a reasoning framework that can model the reasoning process underlying natural language inferences. The framework is based on the semantic tableau…
SpaceNLI: Evaluating the Consistency of Predicting Inferences in Space
Lasha Abzianidze, Joost Zwarts, Yoad Winter
While many natural language inference (NLI) datasets target certain semantic phenomena, e.g., negation, tense & aspect, monotonicity, and presupposition, to the best of our knowled…
A Logic-Based Framework for Natural Language Inference in Dutch
Lasha Abzianidze, Konstantinos Kogkalidis
We present a framework for deriving inference relations between Dutch sentence pairs. The proposed framework relies on logic-based reasoning to produce inspectable proofs leading u…
The Parallel Meaning Bank: A Framework for Semantically Annotating Multiple Languages
Lasha Abzianidze, Rik van Noord, Chunliu Wang +1
This paper gives a general description of the ideas behind the Parallel Meaning Bank, a framework with the aim to provide an easy way to annotate compositional semantics for texts…
DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs
Lasha Abzianidze, Johan Bos, Stephan Oepen
Discourse Representation Theory (DRT) is a formal account for representing the meaning of natural language discourse. Meaning in DRT is modeled via a Discourse Representation Struc…
Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference
Lasha Abzianidze
Tackling Natural Language Inference with a logic-based method is becoming less and less common. While this might have been counterintuitive several decades ago, nowadays it seems p…
Thirty Musts for Meaning Banking
Johan Bos, Lasha Abzianidze
Meaning banking--creating a semantically annotated corpus for the purpose of semantic parsing or generation--is a challenging task. It is quite simple to come up with a complex mea…
The First Shared Task on Discourse Representation Structure Parsing
Lasha Abzianidze, Rik van Noord, Hessel Haagsma +1
The paper presents the IWCS 2019 shared task on semantic parsing where the goal is to produce Discourse Representation Structures (DRSs) for English sentences. DRSs originate from…
Can neural networks understand monotonicity reasoning?
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki +4
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 interactio…
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki +4
Large crowdsourced datasets are widely used for training and evaluating neural models on natural language inference (NLI). Despite these efforts, neural models have a hard time cap…
Exploring Neural Methods for Parsing Discourse Representation Structures
Rik van Noord, Lasha Abzianidze, Antonio Toral +1
Neural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of producing meaning representations based on formal semantics. We…
What can we learn from Semantic Tagging?
Mostafa Abdou, Artur Kulmizev, Vinit Ravishankar +2
We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP ta…
Evaluating Scoped Meaning Representations
Rik van Noord, Lasha Abzianidze, Hessel Haagsma +1
Semantic parsing offers many opportunities to improve natural language understanding. We present a semantically annotated parallel corpus for English, German, Italian, and Dutch wh…
Towards Universal Semantic Tagging
Lasha Abzianidze, Johan Bos
The paper proposes the task of universal semantic tagging---tagging word tokens with language-neutral, semantically informative tags. We argue that the task, with its independent n…
LangPro: Natural Language Theorem Prover
Lasha Abzianidze
LangPro is an automated theorem prover for natural language (https://github.com/kovvalsky/LangPro). Given a set of premises and a hypothesis, it is able to prove semantic relations…
The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations
Lasha Abzianidze, Johannes Bjerva, Kilian Evang +5
The Parallel Meaning Bank is a corpus of translations annotated with shared, formal meaning representations comprising over 11 million words divided over four languages (English, G…