Summary of Multilingual Controlled Generation and Gold-standard-agnostic Evaluation Of Code-mixed Sentences, by Ayushman Gupta et al.
Multilingual Controlled Generation And Gold-Standard-Agnostic Evaluation of Code-Mixed Sentences
by Ayushman Gupta, Akhil Bhogal, Kripabandhu Ghosh
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel method for generating code-mixed text called Controlled Generation, which allows for the creation of multiple semantically equivalent code-mixed sentences from an input English sentence. This is achieved by parameterizing the code-mixing degree (CMD). The authors also introduce a new evaluation metric called GAME (Gold-Standard Agnostic Measure for Evaluation of Code-Mixed Sentences) that is both language-agnostic and gold-standard-agnostic, eliminating the need for human annotators in the evaluation process. This metric is designed to evaluate semantically equivalent code-mixed sentences and has been found to have a lower standard deviation than BLEU scores when used to evaluate such sentences. To encourage further research on code-mixing, the authors release a dataset containing gold-standard code-mixed sentences across 4 language pairs: English-Hindi, English-Bengali, English-French, and English-Spanish. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how computers can be trained to mix different languages together in text. This is a common thing that people do when they speak more than one language. The problem is that there’s no right or wrong way to do it, so it’s hard to measure how good a computer program is at doing it. The authors came up with a new way for computers to mix languages called Controlled Generation. They also created a new way to test how well the computers are doing this called GAME. This new way doesn’t need people to check the answers, which makes it more efficient. The authors think that this will make it easier for other researchers to work on mixing languages with computers. |
Keywords
» Artificial intelligence » Bleu
