Papers by Michele Bevilacqua
Generationary or “How We Went beyond Word Sense Inventories and Learned to Gloss” (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to Word Sense Disambiguation use discrete word senses . however, many language users have different understandings of words . |
| Approach: | They propose a unified computational lexical semantics model that can produce contextually appropriate definitions. |
| Outcome: | The proposed model outperforms existing models in lexical semantics and discriminative tasks. |
Fatality Killed the Cat or: BabelPic, a Multimodal Dataset for Non-Concrete Concepts (2020.acl-main)
Copied to clipboard
| Challenge: | Existing datasets for image understanding do not cover concepts denoting concrete, tangible things such as CAT, TRAFFIC LIGHT and so on. |
| Approach: | They present a hand-labeled image-synset dataset cleaning the BabelNet Lexical Knowledge Base (LKB) they explicitly target non-concrete concepts, thus providing refreshing new data for the community . |
| Outcome: | The proposed dataset cleans the image-synset association within the BabelNet Lexical Knowledge Base (LKB) it explicitly targets non-concrete concepts, providing refreshing new data for the community. |
Nibbling at the Hard Core of Word Sense Disambiguation (2022.acl-long)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is a task that is based on a set of pre-trained language models. |
| Approach: | They propose to use Word Sense Disambiguation to test whether systems can handle ambiguous words. |
| Outcome: | The proposed benchmarks show that seven of the most representative state-of-the-art systems make trivial errors on traditional evaluation benchmarks. |
Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information (2020.acl-main)
Copied to clipboard
| Challenge: | Neural architectures are the current state of the art in Word Sense Disambiguation (WSD) however, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKBs). |
| Approach: | They propose a neural supervised architecture that embeds Lexical Knowledge Bases and exploits pretrained synset embeddings to predict synsets that are not in the training set. |
| Outcome: | The proposed architecture breaks through the 80% ceiling on the concatenation of all standard all-words English evaluation benchmarks. |
Integrating Personalized PageRank into Neural Word Sense Disambiguation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Neural Word Sense Disambiguation (WSD) uses pre-existing knowledge, but only close neighbors influence prediction. |
| Approach: | They propose to exploit WordNet graphs to improve a classification model by recomputing logits . they incorporate an online neural approximated PageRank to refine edge weights . |
| Outcome: | The proposed method improves the current state of the art in the field of Neural Word Sense Disambiguation (WSD) the proposed method exploits the global graph structure while keeping space requirements linear in the number of edges. |
SPRING Goes Online: End-to-End AMR Parsing and Generation (2021.emnlp-demo)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) is a formalism for representing the semantics of natural language in a readable and hierarchical way. |
| Approach: | They present SPRING Online Services, a Web interface and RESTful APIs for their AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation). |
| Outcome: | The proposed system provides a highly interactive visualization platform and feedback mechanism to obtain user suggestions for further improvements of the system’s output. |