Deep-change at AXOLOTL-24: Orchestrating WSD and WSI Models for Semantic Change Modeling
- URL: http://arxiv.org/abs/2408.05184v1
- Date: Fri, 9 Aug 2024 17:15:54 GMT
- Title: Deep-change at AXOLOTL-24: Orchestrating WSD and WSI Models for Semantic Change Modeling
- Authors: Denis Kokosinskii, Mikhail Kuklin, Nikolay Arefyev,
- Abstract summary: This paper describes our solution of the first subtask from the AXOLOTL-24 shared task on Semantic Change Modeling.
We propose and experiment with three new methods solving this task.
We develop a model that can tell if a given word usage is not described by any of the provided sense definitions.
- Score: 0.19116784879310028
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper describes our solution of the first subtask from the AXOLOTL-24 shared task on Semantic Change Modeling. The goal of this subtask is to distribute a given set of usages of a polysemous word from a newer time period between senses of this word from an older time period and clusters representing gained senses of this word. We propose and experiment with three new methods solving this task. Our methods achieve SOTA results according to both official metrics of the first substask. Additionally, we develop a model that can tell if a given word usage is not described by any of the provided sense definitions. This model serves as a component in one of our methods, but can potentially be useful on its own.
Related papers
- AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling [3.556988111507058]
AXOLOTL'24 is the first multilingual explainable semantic change modeling shared task.
We present new sense-annotated diachronic semantic change datasets for Finnish and Russian.
The setup of AXOLOTL'24 is new to the semantic change modeling field.
arXiv Detail & Related papers (2024-07-04T17:41:32Z) - TartuNLP @ AXOLOTL-24: Leveraging Classifier Output for New Sense Detection in Lexical Semantics [0.21485350418225246]
We present our submission to the AXOLOTL-24 shared task.
The task comprises two subtasks: identifying new senses that words gain with time and producing the definitions for the identified new senses.
We trained adapter-based binary classification models to match glosses with usage examples and leveraged the probability output of the models to identify novel senses.
arXiv Detail & Related papers (2024-07-04T11:46:39Z) - Rosetta Stone at KSAA-RD Shared Task: A Hop From Language Modeling To
Word--Definition Alignment [2.6672466522084948]
This work focuses on deriving a vector representation of an Arabic word from its accompanying description.
For the first subtask, our approach relies on an ensemble of finetuned Arabic BERT-based models, predicting the word embedding for a given definition.
In contrast, the most effective solution for the second subtask involves translating the English test definitions into Arabic.
arXiv Detail & Related papers (2023-10-24T13:23:57Z) - A Unified Model for Reverse Dictionary and Definition Modelling [7.353994554197792]
We train a dual-way neural dictionary to guess words from definitions (reverse dictionary) and produce definitions given words (definition modelling)
Our method learns the two tasks simultaneously, and handles unknown words via embeddings.
It casts a word or a definition to the same representation space through a shared layer, then generates the other form from there, in a multi-task fashion.
arXiv Detail & Related papers (2022-05-09T23:52:39Z) - Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing [110.4684789199555]
We introduce scenario-based semantic parsing: a variant of the original task which first requires disambiguating an utterance's "scenario"
This formulation enables us to isolate coarse-grained and fine-grained aspects of the task, each of which we solve with off-the-shelf neural modules.
Our model is modular, differentiable, interpretable, and allows us to garner extra supervision from scenarios.
arXiv Detail & Related papers (2022-02-02T08:00:21Z) - Fake it Till You Make it: Self-Supervised Semantic Shifts for
Monolingual Word Embedding Tasks [58.87961226278285]
We propose a self-supervised approach to model lexical semantic change.
We show that our method can be used for the detection of semantic change with any alignment method.
We illustrate the utility of our techniques using experimental results on three different datasets.
arXiv Detail & Related papers (2021-01-30T18:59:43Z) - SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical
Semantic Change [58.87961226278285]
This paper describes SChME, a method used in SemEval-2020 Task 1 on unsupervised detection of lexical semantic change.
SChME usesa model ensemble combining signals of distributional models (word embeddings) and wordfrequency models where each model casts a vote indicating the probability that a word sufferedsemantic change according to that feature.
arXiv Detail & Related papers (2020-12-02T23:56:34Z) - NLP-CIC @ DIACR-Ita: POS and Neighbor Based Distributional Models for
Lexical Semantic Change in Diachronic Italian Corpora [62.997667081978825]
We present our systems and findings on unsupervised lexical semantic change for the Italian language.
The task is to determine whether a target word has evolved its meaning with time, only relying on raw-text from two time-specific datasets.
We propose two models representing the target words across the periods to predict the changing words using threshold and voting schemes.
arXiv Detail & Related papers (2020-11-07T11:27:18Z) - VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word
Representations for Improved Definition Modeling [24.775371434410328]
We tackle the task of definition modeling, where the goal is to learn to generate definitions of words and phrases.
Existing approaches for this task are discriminative, combining distributional and lexical semantics in an implicit rather than direct way.
We propose a generative model for the task, introducing a continuous latent variable to explicitly model the underlying relationship between a phrase used within a context and its definition.
arXiv Detail & Related papers (2020-10-07T02:48:44Z) - Grounded Compositional Outputs for Adaptive Language Modeling [59.02706635250856]
A language model's vocabulary$-$typically selected before training and permanently fixed later$-$affects its size.
We propose a fully compositional output embedding layer for language models.
To our knowledge, the result is the first word-level language model with a size that does not depend on the training vocabulary.
arXiv Detail & Related papers (2020-09-24T07:21:14Z) - Words aren't enough, their order matters: On the Robustness of Grounding
Visual Referring Expressions [87.33156149634392]
We critically examine RefCOg, a standard benchmark for visual referring expression recognition.
We show that 83.7% of test instances do not require reasoning on linguistic structure.
We propose two methods, one based on contrastive learning and the other based on multi-task learning, to increase the robustness of ViLBERT.
arXiv Detail & Related papers (2020-05-04T17:09:15Z)
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.