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-...
Moreover, we find that the model pre-trained with multi-modal data performs better in the single-modal downstream tasks. We use the General Language Understanding Evaluation (GLUE) benchmark for single-modal tasks to evaluate our model, which outperforms Bidirectional Encoder Representations from ...
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 ...
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 ...
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...
This work aims at understanding the benefits of feature combination procedures rather than comparing SVMs against neural networks, or our system against other architectures. However, Table6shows the performance in term ofF1of the proposed method and other recent architectures. In particular, the achieve...
WLN requires an extensive knowledge and understanding of the notation’s rule. A more intuitive notation, theSimplified Molecular Input Line Entry System(SMILES), was developed in 1988 by Weininger et al. [9] and has been the most popular line notation ever since. SMILES notation system was th...
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. ...
ACNNis a representative architecture for sentence classification in NLP. The CNN was first popularized incomputer visiondue to the advantages of convolutional filters that extract local and translational invariance from the pixel information of an image while reducing parameters through weight sharing (K...