People successfully communicate in everyday situations using vague language. In particular, colour terms have no clear boundaries as to the ranges of colours they describe. We model people's reasoning process in a dyadic reference game using the Rational Speech Acts (RSA) framework and probabilistic semantics, and we find that the implementation of probabilistic semantics requires a modification from pure theory to perform well on real-world data. In addition, we explore approaches to handling target disagreements in reference games, an issue that is rarely discussed in the RSA literature.

Colour Me Uncertain: Representing Vagueness with Probabilistic Semantics

Knowledge Base Embedding (KBE) models have been widely used to encode structured information from knowledge bases, including WordNet. However, the existing literature has predominantly focused on link prediction as the evaluation task, often neglecting exploration of the models' semantic capabilities. In this paper, we investigate the potential disconnect between the performance of KBE models of WordNet on link prediction and their ability to encode semantic information, highlighting the limitations of current evaluation protocols. Our findings reveal that some top-performing KBE models on the WN18RR benchmark exhibit subpar results on two semantic tasks and two downstream tasks. These results demonstrate the inadequacy of link prediction benchmarks for evaluating the semantic capabilities of KBE models, suggesting the need for a more targeted assessment approach.

Are Embedded Potatoes Still Vegetables? On the Limitations of WordNet Embeddings for Lexical Semantics

Functional Distributional Semantics (FDS) models lexical and sentence-level semantics with functions using distributional information. Previous implementations of FDS focus on subject–verb–object (SVO) triples only. We devise computationally efficient and linguistically motivated methods for applying FDS to arbitrary sentences.

Functional Distributional Semantics at Scale

In this work, we explore the novel idea of employing dependency parsing information in the
context of few-shot learning, the task of learning the meaning of a rare word based on a limited amount of context sentences. Firstly, we
use dependency-based word embedding models as background spaces for few-shot learning.
Secondly, we introduce two few-shot learning
methods which enhance the additive baseline
model by using dependencies.

 Using dependency parsing for few-shot learning in distributional semantics

Functional Distributional Semantics is a recently proposed framework for learning distributional semantics that provides linguistic interpretability. We train it on the Visual Genome dataset. our model outperforms previous work on learning semantics from Visual Genome.

Guy Emerson

5

13

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES