51 points by nlpenthusiast 6 months ago flag hide 11 comments
john_doe 6 months ago next
[Title] Positional-encoding-aware transformers yield significant improvements in NLP tasks - exciting new research! I came across this fascinating new research paper that delves into positional-encoding-aware transformers and how they contribute to more accurate NLP tasks. With their ability to understand word positions in sequences, these specialized transformers are able to greatly improve language understanding, even challenging traditional RNN-based models. Can't wait to see how these techniques will be adopted in real-world NLP applications! [URL](https://arxiv.org/abs/xxxx/paper) (via @a_colleague)
sam_the_man 6 months ago next
@john_doe I've heard about these position-aware transformers before, they've had some good results, haven't they? Something about relative and absolute positional encodings, if I remember correctly. Do you think they can overtake LSTMs/GRUs in specific scenarios?
john_doe 6 months ago next
@sam_the_man Definitely, that's how they achieve the improved results! They employ both absolute and relative encodings. Relative encodings help to grasp context, while absolute encodings assure that position information is passed correctly. There are niche scenarios where they might outperform LSTMs and GRUs, especially if contextual information is crucial. However, it's not a given as there's always a trade-off between model architecture and the specific task.
big_data_analyst 6 months ago next
Any idea how long it might take before we see widespread adaptation of positional-encoding-aware transformers in popular libraries like TensorFlow or PyTorch? Would be cool to leverage this as a drop-in replacement while training our models.
nlp_enthusiast 6 months ago prev next
I completely agree with you, @john_doe; it's a very interesting subject, indeed. I've been experimenting with a few implementations lately. I'm using Hugging Face's Transformers library, which has an RoBERTa version that supports these positional encodings. I've seen a slight increase in accuracy when working with Named Entity Recognition tasks. Nothing groundbreaking, but still promising! [URL](https://huggingface.co/transformers/main_classes/model.html#transformers.RobertaForTokenClassification)
ann_deeplearner 6 months ago next
@nlp_enthusiast Have you considered or are you aware of Facebook's Tapas as well? I think it could also be relevant for these tasks. Just a thought! [URL](https://ai.facebook.com/blog/tapas-table-parser-for-natural-language-processing/)
matrixbot 6 months ago prev next
[Comment Bot] Monitoring 7 interesting comments so far related to positional-encoding-aware transformers. Keep the conversation going! #HackerNews #NLP #Transformers
machine_whisperer 6 months ago prev next
Indeed, very intriguing! I'm curious if there are any limitations to these transformer models, especially when it comes to memory and processing.
node_wizard 6 months ago next
@machine_whisperer Yes, you're right. One of their limitations is that they could require a lot more memory and computational resources compared to CNNs or RNNs. To address this challenge, people are looking at methods like sparse attention or tensor train decompositions to reduce these resource requirements. [URL](https://arxiv.org/abs/1904.10509)
elon_musk 6 months ago prev next
Impressive progress in NLP and deep learning overall! Exciting times ahead. Will be interesting to see how these techniques are applied in widespread use-cases.
ai_polymath 6 months ago prev next
From my perspective, these positional-aware transformers are a crucial step towards interpretable models while still achieving high accuracy. More research like this is needed!