Summary of Lexicalization Is All You Need: Examining the Impact Of Lexical Knowledge in a Compositional Qald System, by David Maria Schmidt et al.
Lexicalization Is All You Need: Examining the Impact of Lexical Knowledge in a Compositional QALD System
by David Maria Schmidt, Mohammad Fazleh Elahi, Philipp Cimiano
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates how explicit knowledge about word meanings can improve Question Answering over Linked Data (QALD) systems. The authors argue that one of the key challenges in interpreting natural language questions is bridging the “lexical gap” by mapping query words to vocabulary elements. They propose a compositional QA system that leverages explicit lexical knowledge to infer question meaning and demonstrate its performance on QALD-9, achieving up to 35.8% increase in micro F1 score compared to state-of-the-art systems. The authors also show the limitations of Large Language Models (LLMs) in exploiting lexical knowledge and highlight the importance of compositionality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how knowing what words mean can help answer questions about data linked together. Right now, it’s hard for computers to understand natural language questions because they don’t know which vocabulary words are relevant. The authors suggest a new way to do this by using a system that knows the meanings of individual words and puts them together to figure out what the question is asking. They show that this approach works really well on a specific dataset, called QALD-9, and could be important for understanding data linked together. |
Keywords
» Artificial intelligence » F1 score » Question answering
