Papers with definition generation
Explicit Semantic Decomposition for Definition Generation (2020.acl-main)
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| Challenge: | Existing definition generation methods rely on decoding to extract semantic components of words. |
| Approach: | They propose a method which explicitly decomposes meaning of words into semantic components and models them with discrete latent variables for definition generation. |
| Outcome: | The proposed method outperforms existing methods on WordNet and Oxford benchmarks. |
Fine-grained Contrastive Learning for Definition Generation (2022.aacl-main)
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| Challenge: | Recent pre-trained transformer-based definition generation models lack effective representation learning to contain full semantic components of the given word, leading to under-specific definitions. |
| Approach: | They propose a novel contrastive learning method that encourages the model to capture more detailed semantic representations from the definition sequence encoding. |
| Outcome: | The proposed method could generate more specific definitions compared with state-of-the-art models. |
Definition Modelling for Appropriate Specificity (2021.emnlp-main)
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| Challenge: | Existing definition generation techniques have faced various problems such as the out-of-vocabulary problem and over/under-specificity problems. |
| Approach: | They propose to leverage a pre-trained encoder-decoder model and introduce a re-ranking mechanism to model specificity in definitions. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art method on standard evaluation datasets and shows that it addresses the over/under-specificity problems. |
Graphine: A Dataset for Graph-aware Terminology Definition Generation (2021.emnlp-main)
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| Challenge: | Lack of large-scale terminology definition dataset hinders definition generation . lack of precise terminology definitions poses great challenges in scientific communication . |
| Approach: | They propose a large-scale terminology definition dataset Graphine that exploits the graph structure of terminologies to generate graph-aware text generation models. |
| Outcome: | The proposed model outperforms existing models by exploiting graph structure of terminologies. |
Learning to Describe Unknown Phrases with Local and Global Contexts (N19-1)
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Shonosuke Ishiwatari, Hiroaki Hayashi, Naoki Yoshinaga, Graham Neubig, Shoetsu Sato, Masashi Toyoda, Masaru Kitsuregawa
| Challenge: | Existing methods for contextual guessing and definition generation do not take clues from local contexts. |
| Approach: | They propose a neural description model that takes clues from local and global contexts . they assume that the target phrase is newly emerged and there is no global context . |
| Outcome: | The proposed model takes clues from local and global contexts over existing methods . it is more effective than existing methods for non-standard English explanation . |
Bridging the Defined and the Defining: Exploiting Implicit Lexical Semantic Relations in Definition Modeling (D19-1)
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| Challenge: | Existing definition modeling methods do not utilize lexical semantic relations between defined words and defining words. |
| Approach: | They propose definition modeling methods that use lexical semantic relations . they use unsupervised pattern-based word-pair embeddings that represent semantic relations of word pairs . |
| Outcome: | The proposed methods improve definition generation and learning embeddings from definitions. |
On the Rigour of Scientific Writing: Criteria, Analysis, and Insights (2024.findings-emnlp)
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| Challenge: | despite its importance, little work exists on modelling rigour in scientific writing . despite widespread use of term, scientific literature lacks definition of rigor . |
| Approach: | They propose a framework to automatically identify and define rigour criteria and assess their relevance in scientific writing. |
| Outcome: | The proposed framework can be tailored to the evaluation of scientific rigour for different areas. |
Do Large Language Models Understand Word Senses? (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have set new performance standards in a wide range of tasks. |
| Approach: | They evaluate the Word Sense Disambiguation capabilities of instruction-tuned LLMs and their ability to understand word senses in three generative settings: definition generation, free-form explanation, and example generation. |
| Outcome: | The proposed models can explain the meaning of words in context with 98% accuracy, while demonstrating greater robustness across domains and levels of difficulty. |