Summary of Improving Rare Word Translation with Dictionaries and Attention Masking, by Kenneth J. Sible et al.
Improving Rare Word Translation With Dictionaries and Attention Masking
by Kenneth J. Sible, David Chiang
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 A novel approach is proposed in this paper to address the issue of rare words in machine translation, particularly in low-resource and out-of-domain settings. The dominant encoder-decoder architecture often struggles with these rare words, leading to decreased performance. Human translators typically rely on monolingual or bilingual dictionaries to overcome this challenge. This paper suggests appending definitions from a bilingual dictionary to source sentences and utilizing attention masking to link together rare words with their definitions. Experimental results demonstrate that including definitions for rare words can improve translation quality by up to 1.0 BLEU and 1.6 MacroF1. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps machines translate languages better, especially when they don’t have much information or are translating in new ways. One problem is that some words are very rare and the machine translation system doesn’t know what they mean. Humans solve this by using dictionaries, but machines need a new way to do it. The idea is to add definitions from a dictionary to the original text and then use attention masking to connect these rare words with their meanings. This makes translations better by up to 1 point on a special scoring system. |
Keywords
* Artificial intelligence * Attention * Bleu * Encoder decoder * Translation
