In particular, I think better understanding what information LSTMs and language models will become more important, as they seem to be a key driver of progress in NLP going forward, as evidenced by our ACL paper on language model fine-tuning andrelated approaches. Understanding state-of-the-...
-Predicting missing typological features from multilingual representations -Extending representations to new languages and tasks with minimal supervision -Self-supervised cross-lingual representation learning -Zero-shot or few-shot cross-lingual transfer for language understanding and generation -Automatic large-...
learning, but also to the understanding of information processing and storage in the brain. Distributed representations of data are the de-facto approach for many state-of-the-art deep learning techniques, notably in the area of Natural Language Processing, which will be the focus of this blog ...
Understanding search queries is critical for shopping search engines to\ndeliver a satisfying customer experience. Popular shopping search engines\nreceive billions of unique queries yearly, each of which can depict any of\nhundreds of user preferences or intents. In order to get the right results...
Apologies if this sounds like a stupid question, but I'm just curious. Say I have this: See my understanding of async/await is that the UI becomes responsive as soon as an await is hit. So in theory, ... Printing object attributes based on user input in Python 3x ...
GPT and BERT are both Transformer based. We talked about the transformer structure in this post: In this lecture, the professor shared a useful codelab for understanding transformer: nlp.seas.harvard.edu/20 GPT basically applies a transformer decoder for maximizing P(w_i|w_{i-1}, w_{i-2...
of enantiomers. These findings are expected to deepen the understanding of NLP models in chemistry. Introduction Recent advancements in machine learning have influenced various studies in chemistry such as molecular property prediction, energy calculation, and structure generation1,2,3,4,5,6. To ...
In the past, the models are generally trained on data in a single language (English), and cannot be directly used beyond that language. This is sort of generalize issue. Cross-lingual language understanding (XLU) is hit. They also pay attention to low-resource languages. ...
To evaluate such cross-lingual sentence understanding methods, we built XNLI, an extension of theSNLI/MultiNLIcorpus in 15 languages.XNLI raises the following research question: how can we make predictions in any language at test time, when we only have English training data for that task?
Pre-training of Deep Bidirectional Transformers for Language Understanding 问题:语言模型只使用左上下文或右上下文,但语言理解是双向的 为什么LMs是单向的? 原因1:方向性对于生成格式良好的概率分布是有必要的 我们不在乎这个 原因2:双向编码器中单词可以“看到自己” 解决方案:mask out k % 的输入单词,然后预测...