# Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings

arXiv: https://arxiv.org/abs/2205.13858 · HTML: https://mlsanity.com/paper/2205.13858/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 66 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling](https://mlsanity.com/paper/1312.3005/) (2013)
- [All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality](https://mlsanity.com/paper/2109.04404/) (2021)
- [Distributional Semantics and Linguistic Theory](https://mlsanity.com/paper/1905.01896/) (2019)
- [Pre-Training Transformers as Energy-Based Cloze Models](https://mlsanity.com/paper/2012.08561/) (2020)
- [Definition Modeling: Learning to define word embeddings in natural language](https://mlsanity.com/paper/1612.00394/) (2016)
- [Conditional Generators of Words Definitions](https://mlsanity.com/paper/1806.10090/) (2018)
- [Learning to Understand Phrases by Embedding the Dictionary](https://mlsanity.com/paper/1504.00548/) (2015)
- [Meaning to Form: Measuring Systematicity as Information](https://mlsanity.com/paper/1906.05906/) (2019)
- [Multi-channel Reverse Dictionary Model](https://mlsanity.com/paper/1912.08441/) (2019)
- [Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling](https://mlsanity.com/paper/1911.05715/) (2019)
- [Incorporating Sememes into Chinese Definition Modeling](https://mlsanity.com/paper/1905.06512/) (2019)
- [BERT for Monolingual and Cross-Lingual Reverse Dictionary](https://mlsanity.com/paper/2009.14790/) (2020)
- [Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture](https://mlsanity.com/paper/1606.00025/) (2016)
- [Improving Interpretability of Word Embeddings by Generating Definition and Usage](https://mlsanity.com/paper/1912.05898/) (2019)
- [Evaluating a Multi-sense Definition Generation Model for Multiple Languages](https://mlsanity.com/paper/2006.07398/) (2020)
- [Learning Architectures from an Extended Search Space for Language Modeling](https://mlsanity.com/paper/2005.02593/) (2020)
- [BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling](https://mlsanity.com/paper/2204.07701/) (2022)
- [PREDICT: Persian Reverse Dictionary](https://mlsanity.com/paper/2105.00309/) (2021)
- [IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations](https://mlsanity.com/paper/2205.06840/) (2022)

## Built on by (most cited first, 18 of 18)

- [Separating Tongue from Thought: Activation Patching Reveals Language-Agnostic Concept Representations in Transformers](https://mlsanity.com/paper/2411.08745/) (2024)
- [SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes](https://mlsanity.com/paper/2403.07726/) (2024)
- [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://mlsanity.com/paper/2305.11993/) (2023)
- [Definition generation for lexical semantic change detection](https://mlsanity.com/paper/2406.14167/) (2024)
- [Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions](https://mlsanity.com/paper/2305.07303/) (2023)
- ["Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics](https://mlsanity.com/paper/2306.08433/) (2023)
- [Isotropy, Clusters, and Classifiers](https://mlsanity.com/paper/2402.03191/) (2024)
- [LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spaces](https://mlsanity.com/paper/2312.13208/) (2023)
- [CoLLEGe: Concept Embedding Generation for Large Language Models](https://mlsanity.com/paper/2403.15362/) (2024)
- [Presence or Absence: Are Unknown Word Usages in Dictionaries?](https://mlsanity.com/paper/2406.00656/) (2024)
- [AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling](https://mlsanity.com/paper/2407.04079/) (2024)
- [DORE: A Dataset For Portuguese Definition Generation](https://mlsanity.com/paper/2403.18018/) (2024)
- [3D-EX : A Unified Dataset of Definitions and Dictionary Examples](https://mlsanity.com/paper/2308.03043/) (2023)
- [Enriching Word Usage Graphs with Cluster Definitions](https://mlsanity.com/paper/2403.18024/) (2024)
- [1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Task](https://mlsanity.com/paper/2206.03702/) (2022)
- [WIKITIDE: A Wikipedia-Based Timestamped Definition Pairs Dataset](https://mlsanity.com/paper/2308.03582/) (2023)
- [Deep-change at AXOLOTL-24: Orchestrating WSD and WSI Models for Semantic Change Modeling](https://mlsanity.com/paper/2408.05184/) (2024)
- [MURAD: A Large-Scale Multi-Domain Unified Reverse Arabic Dictionary Dataset](https://mlsanity.com/paper/2601.21512/) (2026)

## Abstract (arXiv)

Word embeddings have advanced the state of the art in NLP across numerous tasks. Understanding the contents of dense neural representations is of utmost interest to the computational semantics community. We propose to focus on relating these opaque word vectors with human-readable definitions, as found in dictionaries. This problem naturally divides into two subtasks: converting definitions into embeddings, and converting embeddings into definitions. This task was conducted in a multilingual setting, using comparable sets of embeddings trained homogeneously.