Summary of Predicting the Target Word Of Game-playing Conversations Using a Low-rank Dialect Adapter For Decoder Models, by Dipankar Srirag et al.
Predicting the Target Word of Game-playing Conversations using a Low-Rank Dialect Adapter for Decoder Models
by Dipankar Srirag, Aditya Joshi, Jacob Eisenstein
First submitted to arxiv on: 31 Aug 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 A novel architecture called LoRDD is introduced to extend the concept of dialect adapters to decoder models for Natural Language Understanding (NLU) tasks on sociolects. The proposed approach combines task adapters and dialect adapters, leveraging contrastive learning on pseudo-parallel conversations from the MD-3 dataset. Experiments demonstrate that LoRDD outperforms four baselines in Target Word Prediction (TWP), reducing the performance gap with American English by 12% to 5.8% for word similarity and 25% to 4.5% for accuracy. The focused contribution of LoRDD lies in its potential for dialect adaptation of decoder models using TWP, a simplified version of next-word prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoRDD is a new way to make computer models better at understanding different types of languages. Right now, these models are mostly trained on American English and don’t do well with other dialects or languages. This paper shows that LoRDD can help bridge the gap by improving how well models understand Indian English, Nigerian English, and other languages. It’s like teaching a model to learn new words and phrases from different cultures. |
Keywords
» Artificial intelligence » Decoder » Language understanding
