Summary of A Truly Joint Neural Architecture For Segmentation and Parsing, by Danit Yshaayahu Levi and Reut Tsarfaty
A Truly Joint Neural Architecture for Segmentation and Parsing
by Danit Yshaayahu Levi, Reut Tsarfaty
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper proposes a joint neural architecture that simultaneously solves morphological segmentation and syntactic parsing tasks in Morphologically Rich Languages (MRLs). The current state-of-the-art parsers employ a strict pipeline approach, whereas this proposed method utilizes a lattice-based representation to preserve input ambiguity. The model is language-agnostic and leverages Long-Short Term Memory (LLM) components. Experiments on Hebrew, a highly ambiguous MRL, demonstrate state-of-the-art performance in parsing, tagging, and segmentation tasks using a single model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to understand languages that are hard to parse because of the way they are written. It proposes a special kind of computer program that can look at words as a combination of sounds and meanings, rather than just individual letters or sounds. This helps the program understand the structure of sentences better, which is important for tasks like translating or summarizing text. The program was tested on Hebrew, a language that is particularly challenging because it has many different forms of the same word. The results showed that this new approach can be very effective and could help improve our understanding of languages in general. |
Keywords
* Artificial intelligence * Parsing
