Next word predictionLSTMRNNAssameseIn this paper, we present a Long Short Term Memory network (LSTM) model which is a special kind of Recurrent Neural Net-work(RNN) for instant messaging, where the goal is to p
We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language. Our model is a causal transformer trained via autoregressive prediction of sensorimotor trajectories. To account for the multi-modal nature of the data, we perform prediction in ...
Recent studies have started to discuss the impact of sequence length on the prediction performance with LSTM models (Feng et al., 2018, Xu et al., 2022). However, they often consider a fixed length for every input sequence, without distinguishing between users and the temporal context of the...
However, there are still several challenges that remain to be addressed in this regard. Although datasets contain a large number of check-in records, user preferences are relatively fixed, leading to extremely sparse interactions between users and POIs. Moreover, sequential model learns each user’s...
The performances of PRME, Time-LSTM, and GETNext are unstable and even worse than RNN on several datasets. The check-in dataset is much sparser under the cross-city scenario, so the time intervals between two consecutive check-ins will vary greatly. In this case, the assumption of PRME th...
LSTM and Markov chains and their hybrid were chosen for next-word prediction. Their sequential nature (current output depends on previous) helps to successfully cope with the next-word prediction task. The Markov chains presented the fastest and adequate results. The hybrid model...
LSTMBiLSTMCNNThis paper presents a novel architecture for predicting the next word in bilingual Punjabi-English (BPE) social media texts. The goal is to enhance the performance and accuracy of next-word prediction in multilingual environments. Our proposed model, called NWP-CB (Next-Word ...
Long short-term memory (LSTM)Natural language processing (NLP)Dzongkha typing is time-consuming. A word in Dzongkha is formed by either a single syllable or multiple syllables. A single syllable (property) and multiple syllabic word (cloudy) require 6 and 22 keypresses respectively. Similarly, ...
This paper suggests a technique that predicts the next most relevant and acceptable word in Bangla, the eighth most speaking language in the world. We applied two Recurrent Neural Network models named Bi-directional LSTM (Long Short-Term Memory) and Bidirectional GRU (Gated Recurrent Unit) as ...
The experimental results show that, compared with deep learning models such as SPEED-LSTM, CNN-BiLSTM and BiLSTM-ATT, Word2Vec-BiLSTM and BERT-BiLSTM models have better prediction accuracy, precision, recall and F1 evaluation indicators. Effect. The correct rate of using Word2Vec-BiLSTM on ...